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	<id>https://mw.hh.se/caisr/api.php?action=feedcontributions&amp;feedformat=atom&amp;user=Siddhartha</id>
	<title>ISLAB/CAISR - User contributions [en]</title>
	<link rel="self" type="application/atom+xml" href="https://mw.hh.se/caisr/api.php?action=feedcontributions&amp;feedformat=atom&amp;user=Siddhartha"/>
	<link rel="alternate" type="text/html" href="https://mw.hh.se/caisr/index.php?title=Special:Contributions/Siddhartha"/>
	<updated>2026-04-05T04:53:19Z</updated>
	<subtitle>User contributions</subtitle>
	<generator>MediaWiki 1.35.13</generator>
	<entry>
		<id>https://mw.hh.se/caisr/index.php?title=Siddhartha_Khandelwal&amp;diff=4827</id>
		<title>Siddhartha Khandelwal</title>
		<link rel="alternate" type="text/html" href="https://mw.hh.se/caisr/index.php?title=Siddhartha_Khandelwal&amp;diff=4827"/>
		<updated>2021-04-02T06:50:46Z</updated>

		<summary type="html">&lt;p&gt;Siddhartha: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;{{Person&lt;br /&gt;
|Family Name=Khandelwal&lt;br /&gt;
|Given Name=Siddhartha&lt;br /&gt;
|Title=PhD&lt;br /&gt;
|Email=hello@vectorizemove.com&lt;br /&gt;
|Image=Sid_profile2.jpg&lt;br /&gt;
|Country=Sweden&lt;br /&gt;
|Subject=Human Motion Analysis using Wearable Sensors&lt;br /&gt;
}}&lt;br /&gt;
{{AssignSubjectAreas&lt;br /&gt;
|SubjectArea=Signal Analysis&lt;br /&gt;
}}&lt;br /&gt;
{{AssignApplicationAreas&lt;br /&gt;
|ApplicationArea=Health Technology&lt;br /&gt;
}}&lt;br /&gt;
{{AssignExaminer&lt;br /&gt;
|Examiner=Matlab with Applications (7.5 credits)&lt;br /&gt;
}}&lt;br /&gt;
{{AssignExaminer&lt;br /&gt;
|Examiner=Robotic Manipulators (Teaching Assistant, 7.5 credits)&lt;br /&gt;
}}&lt;br /&gt;
[[Category:alumni]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;!--Remove or add comments --&amp;gt;&lt;br /&gt;
{{ShowPerson}}&lt;br /&gt;
{{InsertTeacher}}&lt;br /&gt;
{{InsertSubjAreas}}&lt;br /&gt;
{{InsertProjects}}&lt;br /&gt;
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&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== About Siddhartha ==&lt;br /&gt;
&lt;br /&gt;
Currently, Siddhartha is the CEO at VectorizeMove: a start-up aimed at bringing the benefits of advanced gait analysis to peoples&amp;#039; everyday lives. More at: www.vectorizemove.com&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== Siddhartha elsewhere == &lt;br /&gt;
&lt;br /&gt;
[http://scholar.google.se/citations?user=7uLMICoAAAAJ&amp;amp;hl=en Google Scholar] &lt;br /&gt;
&lt;br /&gt;
[https://www.linkedin.com/in/siddharthakhandelwal/ LinkedIn] &lt;br /&gt;
&lt;br /&gt;
[http://www.researchgate.net/profile/Siddhartha_Khandelwal/ ResearchGate]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== MAREA Gait Database ==&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
MAREA gait database:  https://wiki.hh.se/caisr/index.php/Gait_database &lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
{{PublicationsList}}&lt;/div&gt;</summary>
		<author><name>Siddhartha</name></author>
	</entry>
	<entry>
		<id>https://mw.hh.se/caisr/index.php?title=File:Sid_profile2.jpg&amp;diff=4826</id>
		<title>File:Sid profile2.jpg</title>
		<link rel="alternate" type="text/html" href="https://mw.hh.se/caisr/index.php?title=File:Sid_profile2.jpg&amp;diff=4826"/>
		<updated>2021-04-02T06:49:57Z</updated>

		<summary type="html">&lt;p&gt;Siddhartha: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;&lt;/div&gt;</summary>
		<author><name>Siddhartha</name></author>
	</entry>
	<entry>
		<id>https://mw.hh.se/caisr/index.php?title=Siddhartha_Khandelwal&amp;diff=4825</id>
		<title>Siddhartha Khandelwal</title>
		<link rel="alternate" type="text/html" href="https://mw.hh.se/caisr/index.php?title=Siddhartha_Khandelwal&amp;diff=4825"/>
		<updated>2021-04-02T06:46:52Z</updated>

		<summary type="html">&lt;p&gt;Siddhartha: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;{{Person&lt;br /&gt;
|Family Name=Khandelwal&lt;br /&gt;
|Given Name=Siddhartha&lt;br /&gt;
|Title=PhD&lt;br /&gt;
|Email=hello@vectorizemove.com&lt;br /&gt;
|Image=Sid.jpg&lt;br /&gt;
|Country=Sweden&lt;br /&gt;
|Subject=Human Motion Analysis using Wearable Sensors&lt;br /&gt;
}}&lt;br /&gt;
{{AssignSubjectAreas&lt;br /&gt;
|SubjectArea=Signal Analysis&lt;br /&gt;
}}&lt;br /&gt;
{{AssignApplicationAreas&lt;br /&gt;
|ApplicationArea=Health Technology&lt;br /&gt;
}}&lt;br /&gt;
{{AssignExaminer&lt;br /&gt;
|Examiner=Matlab with Applications (7.5 credits)&lt;br /&gt;
}}&lt;br /&gt;
{{AssignExaminer&lt;br /&gt;
|Examiner=Robotic Manipulators (Teaching Assistant, 7.5 credits)&lt;br /&gt;
}}&lt;br /&gt;
[[Category:alumni]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;!--Remove or add comments --&amp;gt;&lt;br /&gt;
{{ShowPerson}}&lt;br /&gt;
{{InsertTeacher}}&lt;br /&gt;
{{InsertSubjAreas}}&lt;br /&gt;
{{InsertProjects}}&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== About Siddhartha ==&lt;br /&gt;
&lt;br /&gt;
Currently, Siddhartha is the CEO at VectorizeMove: a start-up aimed at bringing the benefits of advanced gait analysis to peoples&amp;#039; everyday lives. More at: www.vectorizemove.com&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== Siddhartha elsewhere == &lt;br /&gt;
&lt;br /&gt;
[http://scholar.google.se/citations?user=7uLMICoAAAAJ&amp;amp;hl=en Google Scholar] &lt;br /&gt;
&lt;br /&gt;
[https://www.linkedin.com/in/siddharthakhandelwal/ LinkedIn] &lt;br /&gt;
&lt;br /&gt;
[http://www.researchgate.net/profile/Siddhartha_Khandelwal/ ResearchGate]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== MAREA Gait Database ==&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
MAREA gait database:  https://wiki.hh.se/caisr/index.php/Gait_database &lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
{{PublicationsList}}&lt;/div&gt;</summary>
		<author><name>Siddhartha</name></author>
	</entry>
	<entry>
		<id>https://mw.hh.se/caisr/index.php?title=Siddhartha_Khandelwal&amp;diff=4824</id>
		<title>Siddhartha Khandelwal</title>
		<link rel="alternate" type="text/html" href="https://mw.hh.se/caisr/index.php?title=Siddhartha_Khandelwal&amp;diff=4824"/>
		<updated>2021-04-02T06:45:29Z</updated>

		<summary type="html">&lt;p&gt;Siddhartha: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;{{Person&lt;br /&gt;
|Family Name=Khandelwal&lt;br /&gt;
|Given Name=Siddhartha&lt;br /&gt;
|Title=PhD&lt;br /&gt;
|Email=hello@vectorizemove.com&lt;br /&gt;
|Image=Sid.jpg&lt;br /&gt;
|Country=Sweden&lt;br /&gt;
|Subject=Human Motion Analysis using Wearable Sensors&lt;br /&gt;
}}&lt;br /&gt;
{{AssignSubjectAreas&lt;br /&gt;
|SubjectArea=Signal Analysis&lt;br /&gt;
}}&lt;br /&gt;
{{AssignApplicationAreas&lt;br /&gt;
|ApplicationArea=Health Technology&lt;br /&gt;
}}&lt;br /&gt;
{{AssignExaminer&lt;br /&gt;
|Examiner=Matlab with Applications (7.5 credits)&lt;br /&gt;
}}&lt;br /&gt;
{{AssignExaminer&lt;br /&gt;
|Examiner=Robotic Manipulators (Teaching Assistant, 7.5 credits)&lt;br /&gt;
}}&lt;br /&gt;
[[Category:alumni]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;!--Remove or add comments --&amp;gt;&lt;br /&gt;
{{ShowPerson}}&lt;br /&gt;
{{InsertTeacher}}&lt;br /&gt;
{{InsertSubjAreas}}&lt;br /&gt;
{{InsertProjects}}&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== About Siddhartha ==&lt;br /&gt;
&lt;br /&gt;
Currently, he is the CEO of VectorizeMove: a start-up aimed at bringing the benefits of advanced gait analysis to peoples&amp;#039; everyday lives. Read more at: www.vectorizemove.com&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== Siddhartha elsewhere == &lt;br /&gt;
&lt;br /&gt;
[http://scholar.google.se/citations?user=7uLMICoAAAAJ&amp;amp;hl=en Google Scholar] &lt;br /&gt;
&lt;br /&gt;
[https://www.linkedin.com/in/siddharthakhandelwal/ LinkedIn] &lt;br /&gt;
&lt;br /&gt;
[http://www.researchgate.net/profile/Siddhartha_Khandelwal/ ResearchGate]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== MAREA Gait Database ==&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
MAREA gait database:  https://wiki.hh.se/caisr/index.php/Gait_database &lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
{{PublicationsList}}&lt;/div&gt;</summary>
		<author><name>Siddhartha</name></author>
	</entry>
	<entry>
		<id>https://mw.hh.se/caisr/index.php?title=Gait_database&amp;diff=4814</id>
		<title>Gait database</title>
		<link rel="alternate" type="text/html" href="https://mw.hh.se/caisr/index.php?title=Gait_database&amp;diff=4814"/>
		<updated>2021-03-02T13:18:03Z</updated>

		<summary type="html">&lt;p&gt;Siddhartha: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== MAREA (&amp;#039;&amp;#039;&amp;#039;M&amp;#039;&amp;#039;&amp;#039;ovement &amp;#039;&amp;#039;&amp;#039;A&amp;#039;&amp;#039;&amp;#039;nalysis in &amp;#039;&amp;#039;&amp;#039;R&amp;#039;&amp;#039;&amp;#039;eal-world &amp;#039;&amp;#039;&amp;#039;E&amp;#039;&amp;#039;&amp;#039;nvironments using &amp;#039;&amp;#039;&amp;#039;A&amp;#039;&amp;#039;&amp;#039;ccelerometers) Gait Database ==&lt;br /&gt;
&lt;br /&gt;
[[File:accPlacement.jpg|right|thumb|200px|caption|&amp;quot;Figure 1: Position and orientation of each accelerometer at the beginning of each experiment. The accelerometer at the right ankle is attached arbitrarily without any pre-defined orientation. The FSRs are instrumented into the shoe soles to collect the ground truth. The data from all sensors is sampled at a frequency of 128 Hz.&amp;quot;]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
The &amp;#039;&amp;#039;&amp;#039;MAREA&amp;#039;&amp;#039;&amp;#039; gait database comprises of gait activities in different real-world environments as shown in the table below. 20 healthy adults (12 males and 8 females, average age: 33.4 +- 7 years, average mass: 73.2 +- 10.9 kg, average height: 172.6 +- 9.5 cm) participated in the study that was approved by the Ethical Review Board of Lund, Sweden. Each subject had a 3-axes Shimmer3 (Shimmer Research, Dublin, Ireland) accelerometer (+- 8g) attached to their waist, left wrist and left and right ankles using elastic bands and velcro straps. Figure 1 shows the position and orientation of each accelerometer at the beginning of each experiment. On the waist, the accelerometer X and Y axes were pointing to the lateral and downward direction, respectively. On the wrist and left ankle, the Z axis was pointing in the lateral direction while the Y axis was pointing downward and was aligned with the limb longitudinal axis. In order to simulate a lesser controlled scenario, the accelerometer on right ankle was positioned such that the Y axis was pointing downward but the Z axis was marginally disturbed such that it was not exactly perpendicular to the sagittal plane. The subjects were provided shoes that were instrumented with piezo-electric force sensitive resistors (FSRs), fixed at the extreme ends of the sole in order to provide the ground truth values for HS and TO. An external expansion board was used to synchronously collect the data from the FSRs on each foot and the respective ankle accelerometer, at a sampling frequency of 128Hz, and stored locally on the Shimmer3 microSD card. Due to the lack of a centralized data logger, the waist accelerometer was not in perfect synchronization with the ankle accelerometer.&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
{| class=&amp;quot;wikitable&amp;quot;&lt;br /&gt;
! style=&amp;quot;text-align: center;&amp;quot; | Subjects&lt;br /&gt;
! style=&amp;quot;text-align: center;&amp;quot; | Environment&lt;br /&gt;
! style=&amp;quot;text-align: center;&amp;quot; | Activity&lt;br /&gt;
! style=&amp;quot;text-align: center;&amp;quot; | Speed&lt;br /&gt;
! style=&amp;quot;text-align: center;&amp;quot; | Duration&lt;br /&gt;
! style=&amp;quot;text-align: center;&amp;quot; | Short Description&lt;br /&gt;
|-&lt;br /&gt;
| rowspan=&amp;quot;4&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | 11&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | Treadmill (flat)&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | Walk &amp;amp; run&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | 4km/hr - 8km/hr; increasing in steps&lt;br /&gt;
 of 0.4km/hr every minute&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | 10 min&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | Start walking and switch to &lt;br /&gt;
running at self-selected speed&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | Treadmill (slope)&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | Walk&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | Self-selected&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | 12 min&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | Treadmill is set to (5, 0, 10, 0, 15, 0) degree&lt;br /&gt;
inclinations with 2 mins at each angle&lt;br /&gt;
|-&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Indoor flat space&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Walk &amp;amp; run&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Self-selected&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | 6 min&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Start walking and switch &lt;br /&gt;
to running after 3 mins&lt;br /&gt;
|-&lt;br /&gt;
 |&lt;br /&gt;
|-&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | 9&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Outdoor street&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Walk &amp;amp; run&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Self-selected&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | 6 min&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Start walking and switch &lt;br /&gt;
to running after 3 mins&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
|}&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
==&amp;#039;&amp;#039;&amp;#039;Explanation of the database files&amp;#039;&amp;#039;&amp;#039; (download link below)==&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
=== 1. Activity Timings === &lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
There are two matrices provided, namely, &amp;#039;&amp;#039;&amp;#039;Indoor Experiment timings&amp;#039;&amp;#039;&amp;#039; and  &amp;#039;&amp;#039;&amp;#039;Outdoor Experiment timings&amp;#039;&amp;#039;&amp;#039; which contain the timing information of the activities and are included in a folder called Activity Timings.zip: &lt;br /&gt;
&lt;br /&gt;
[[File:ActivityTimingsGraph.png|right|thumb|500px|caption|&amp;quot;Example of how to use the sample numbers given in Indoor Experiment Timings matrix to extract desired activity data. This figure shows the entire Indoor data collected from accelerometer positioned at Right Ankle for Subject 5. &amp;quot;]]&lt;br /&gt;
&lt;br /&gt;
* &amp;#039;&amp;#039;&amp;#039;Indoor Experiment Timings&amp;#039;&amp;#039;&amp;#039;: This 11(rows) x 8(columns) matrix consists of &amp;#039;&amp;#039;&amp;#039;sample numbers&amp;#039;&amp;#039;&amp;#039; corresponding to the start and end of an activity, for a given subject, for the &amp;#039;&amp;#039;&amp;#039;Indoor Experiments&amp;#039;&amp;#039;&amp;#039;, i.e. &lt;br /&gt;
**Treadmill (flat)&lt;br /&gt;
**Treadmill (slope) &lt;br /&gt;
**Indoor flat space. &lt;br /&gt;
The figure below explains how to extract the sample nos. corresponding to an activity for a given subject, from the Indoor Experiment timings matrix.  &lt;br /&gt;
&lt;br /&gt;
[[File:Indoor Table.png|700px]]&lt;br /&gt;
&lt;br /&gt;
* &amp;#039;&amp;#039;&amp;#039;Outdoor Experiment Timings&amp;#039;&amp;#039;&amp;#039;: This 9(rows) x 3(columns) matrix consists of &amp;#039;&amp;#039;&amp;#039;sample numbers&amp;#039;&amp;#039;&amp;#039; corresponding to the start and end of an activity, for a given subject, for the &amp;#039;&amp;#039;&amp;#039;Outdoor Street Experiments&amp;#039;&amp;#039;&amp;#039;. The figure below explains how to extract the sample nos. corresponding to an activity for a given subject, from the Outdoor Experiment timings matrix.&lt;br /&gt;
&lt;br /&gt;
[[File:Outdoor Table.png|800px]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
=== 2. Subject Data === &lt;br /&gt;
&lt;br /&gt;
[[File:AccXYZ.png|right|thumb|400px|caption|&amp;quot;Figure 2: Example of the accelerometer data from the X, Y and Z axis for Sub5_RF&amp;quot;]]&lt;br /&gt;
&lt;br /&gt;
The Subject data files are provided in two formats, namely, .mat format (&amp;#039;&amp;#039;&amp;#039;Subject Data_mat format.zip&amp;#039;&amp;#039;&amp;#039;) and .txt format &amp;#039;&amp;#039;&amp;#039;Subject Data_txt format).zip&amp;#039;&amp;#039;&amp;#039;. The naming convention of the files is: Sub&amp;lt;number&amp;gt;_&amp;lt;position&amp;gt;, where:&lt;br /&gt;
*&amp;lt;number&amp;gt;: stands for Subject number and ranges from 1 to 20. &lt;br /&gt;
*&amp;lt;position&amp;gt;: stands for the position of the accelerometer on the body as shown in Figure 1. The positions are:&lt;br /&gt;
**LF - Left Ankle&lt;br /&gt;
**RF - Right Ankle&lt;br /&gt;
**Wrist&lt;br /&gt;
**Waist&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
For each Subject file, eg. Sub5_RF, the accelerometer data from the 3 axis accelerometer is stored in 3 columns (separated using comma in the .txt files), each named as: &lt;br /&gt;
*accX - data from X - axis&lt;br /&gt;
*accY - data from Y - axis&lt;br /&gt;
*accZ - data from Z - axis&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
===3. Ground Truth===&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
The Ground Truth is provided as a 11x7 structure that gives the timing of the Heel-Strike and Toe-Off events extracted from the FSR signals. This timing information is provided in terms of sample numbers and is relative to each activity. The structure consists of 7 fields that have &amp;#039;&amp;#039;&amp;#039;already been segregated&amp;#039;&amp;#039;&amp;#039; using the Indoor Experiment Timings and Outdoor Experiment Timings explained above: &lt;br /&gt;
*treadWalk       - treadmill (flat) walk &lt;br /&gt;
&lt;br /&gt;
*treadIncline    - treadmill (slope) walk&lt;br /&gt;
&lt;br /&gt;
*treadWalknRun   - treadmill (flat) walk &amp;amp; run&lt;br /&gt;
&lt;br /&gt;
*indoorWalk      - indoor (flat space) walk&lt;br /&gt;
&lt;br /&gt;
*indoorWalknRun  - indoor (flat space) walk &amp;amp; run&lt;br /&gt;
&lt;br /&gt;
*outdoorWalk     - outdoor street walk&lt;br /&gt;
&lt;br /&gt;
*outdoorWalknRun - outdoor street walk &amp;amp; run &lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
Each row of the structure represents a Subject. For Indoor activities, Row 1 -&amp;gt; Subject1, ..., Row 11 -&amp;gt; Subject 11 and so on. For outdoor activities, Row 1 -&amp;gt; Subject 12,..., Row 9 -&amp;gt; Subject 20. As an example: GroundTruth(1).treadWalk provides the ground truth data for Subject 1 for treadmill (flat) walk. This is a structure with fields: &lt;br /&gt;
* SubIdx - Subject number. Ranges from Sub1 - Sub20&lt;br /&gt;
&lt;br /&gt;
* LF_HS - Sample numbers of Heel-Strike event from Left Foot FSR signal.&lt;br /&gt;
&lt;br /&gt;
* LF_TO - Sample numbers of Toe-Off event from Left Foot FSR signal.&lt;br /&gt;
&lt;br /&gt;
* RF_HS - Sample numbers of Heel-Strike event  from Right Foot FSR signal. &lt;br /&gt;
&lt;br /&gt;
* RF_TO - Sample numbers of Toe-Off event from Right Foot FSR signal.&lt;br /&gt;
&lt;br /&gt;
[[File:GroundTruthStructure.png|1200px]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
=== 4. mainScript.m === &lt;br /&gt;
&lt;br /&gt;
This MATLAB script is an example on how to:&lt;br /&gt;
&lt;br /&gt;
* Read the accelerometer data of a subject for an activity &amp;amp; position&lt;br /&gt;
&lt;br /&gt;
* Read the Ground Truth Heel-Strike and Toe-Off gait events for the same&lt;br /&gt;
&lt;br /&gt;
* Plot all the signals&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
----&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 160%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;How to get the datasets?&amp;#039;&amp;#039;&amp;#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
&lt;br /&gt;
----------------------------------------------------------------------------------------------------------------------------------&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 110%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;1. Link to the Release Agreement and fill Registration Form:&amp;#039;&amp;#039;&amp;#039;  [https://wiki.hh.se/caisr/index.php/Gait_database:_Release_agreement Release agreement and Terms of Usage]&amp;lt;/div&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 110%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;2. Link to download the datasets:&amp;#039;&amp;#039;&amp;#039;  Will be sent by Email after Registration Procedure is completed &amp;lt;/div&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 160%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;References&amp;#039;&amp;#039;&amp;#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
----------------------------------------------------&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 120%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;Please remember to include an appropriate citation to acknowledge the use of the database in all documents and papers that uses the MAREA Gait Database:&amp;#039;&amp;#039;&amp;#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
1. Siddhartha Khandelwal, Nicholas Wickström, Evaluation of the performance of accelerometer-based gait event detection algorithms in different real-world scenarios using the MAREA gait database, Gait &amp;amp; Posture, Volume 51, January 2017, Pages 84-90, ISSN 0966-6362, http://dx.doi.org/10.1016/j.gaitpost.2016.09.023.&lt;br /&gt;
&lt;br /&gt;
Sciencedirect: [http://www.sciencedirect.com/science/article/pii/S0966636216305859]&lt;br /&gt;
Diva: [http://hh.diva-portal.org/smash/record.jsf?searchId=1&amp;amp;pid=diva2%3A999938&amp;amp;dswid=1416]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 120%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;You may also want to read:&amp;#039;&amp;#039;&amp;#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
2. Siddhartha Khandelwal; Nicholas Wickström, &amp;quot;Gait Event Detection in Real-World Environment for Long-Term Applications: Incorporating Domain Knowledge into Time-Frequency Analysis,&amp;quot; in IEEE Transactions on Neural Systems and Rehabilitation Engineering , vol. 24, no. 12, pp.1363-1372, 2016 &lt;br /&gt;
&lt;br /&gt;
IEEE url: [http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7423805&amp;amp;newsearch=true&amp;amp;queryText=siddhartha%20khandelwal]&lt;br /&gt;
DiVA url: [http://hh.diva-portal.org/smash/record.jsf?searchId=1&amp;amp;pid=diva2:909015]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
3. Siddhartha Khandelwal, Nicholas Wickström, &amp;quot;Novel methodology for estimating Initial Contact events from accelerometers positioned at different body locations&amp;quot;, in Gait &amp;amp; Posture, Volume 59, Pages 278–285, Jan 2018.&lt;br /&gt;
&lt;br /&gt;
DOI: http://dx.doi.org/10.1016/j.gaitpost.2017.07.030&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
4. Siddhartha Khandelwal; Nicholas Wickström, &amp;quot;Identification of Gait Events using Expert Knowledge and Continuous Wavelet Transform Analysis&amp;quot;, in BIOSIGNALS 2014, 7th International Conference on Bio-inspired Systems and Signal Processing, Angers, France, March 3-6, 2014 &lt;br /&gt;
&lt;br /&gt;
DiVA url: [http://hh.diva-portal.org/smash/record.jsf?searchId=1&amp;amp;pid=diva2:688909]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
5. Siddhartha Khandelwal, Nicholas Wickström, &amp;quot;Detecting Gait Events from Outdoor Accelerometer Data for Long-term and Continuous Monitoring Applications&amp;quot; in 13th International Symposium on 3D Analysis of Human Movement (3D-AHM 2014), 14–17 July, 2014, Lausanne, Switzerland.&lt;br /&gt;
&lt;br /&gt;
DiVA url: [http://www.diva-portal.org/smash/record.jsf?pid=diva2:735193]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 160%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;Created By&amp;#039;&amp;#039;&amp;#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
----------------------------------------------------&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
[http://islab.hh.se/mediawiki/Siddhartha_Khandelwal Siddhartha Khandelwal]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
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&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
----&lt;/div&gt;</summary>
		<author><name>Siddhartha</name></author>
	</entry>
	<entry>
		<id>https://mw.hh.se/caisr/index.php?title=Gait_database&amp;diff=4813</id>
		<title>Gait database</title>
		<link rel="alternate" type="text/html" href="https://mw.hh.se/caisr/index.php?title=Gait_database&amp;diff=4813"/>
		<updated>2021-03-02T13:14:56Z</updated>

		<summary type="html">&lt;p&gt;Siddhartha: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== MAREA (&amp;#039;&amp;#039;&amp;#039;M&amp;#039;&amp;#039;&amp;#039;ovement &amp;#039;&amp;#039;&amp;#039;A&amp;#039;&amp;#039;&amp;#039;nalysis in &amp;#039;&amp;#039;&amp;#039;R&amp;#039;&amp;#039;&amp;#039;eal-world &amp;#039;&amp;#039;&amp;#039;E&amp;#039;&amp;#039;&amp;#039;nvironments using &amp;#039;&amp;#039;&amp;#039;A&amp;#039;&amp;#039;&amp;#039;ccelerometers) Gait Database ==&lt;br /&gt;
&lt;br /&gt;
[[File:accPlacement.jpg|right|thumb|200px|caption|&amp;quot;Figure 1: Position and orientation of each accelerometer at the beginning of each experiment. The accelerometer at the right ankle is attached arbitrarily without any pre-defined orientation. The FSRs are instrumented into the shoe soles to collect the ground truth. The data from all sensors is sampled at a frequency of 128 Hz.&amp;quot;]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
The &amp;#039;&amp;#039;&amp;#039;MAREA&amp;#039;&amp;#039;&amp;#039; gait database comprises of gait activities in different real-world environments as shown in the table below. 20 healthy adults (12 males and 8 females, average age: 33.4 +- 7 years, average mass: 73.2 +- 10.9 kg, average height: 172.6 +- 9.5 cm) participated in the study that was approved by the Ethical Review Board of Lund, Sweden. Each subject had a 3-axes Shimmer3 (Shimmer Research, Dublin, Ireland) accelerometer (+- 8g) attached to their waist, left wrist and left and right ankles using elastic bands and velcro straps. Figure 1 shows the position and orientation of each accelerometer at the beginning of each experiment. On the waist, the accelerometer X and Y axes were pointing to the lateral and downward direction, respectively. On the wrist and left ankle, the Z axis was pointing in the lateral direction while the Y axis was pointing downward and was aligned with the limb longitudinal axis. In order to simulate a lesser controlled scenario, the accelerometer on right ankle was positioned such that the Y axis was pointing downward but the Z axis was marginally disturbed such that it was not exactly perpendicular to the sagittal plane. The subjects were provided shoes that were instrumented with piezo-electric force sensitive resistors (FSRs), fixed at the extreme ends of the sole in order to provide the ground truth values for HS and TO. An external expansion board was used to synchronously collect the data from the FSRs on each foot and the respective ankle accelerometer, at a sampling frequency of 128Hz, and stored locally on the Shimmer3 microSD card. Due to the lack of a centralized data logger, the waist accelerometer was not in perfect synchronization with the ankle accelerometer.&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
{| class=&amp;quot;wikitable&amp;quot;&lt;br /&gt;
! style=&amp;quot;text-align: center;&amp;quot; | Subjects&lt;br /&gt;
! style=&amp;quot;text-align: center;&amp;quot; | Environment&lt;br /&gt;
! style=&amp;quot;text-align: center;&amp;quot; | Activity&lt;br /&gt;
! style=&amp;quot;text-align: center;&amp;quot; | Speed&lt;br /&gt;
! style=&amp;quot;text-align: center;&amp;quot; | Duration&lt;br /&gt;
! style=&amp;quot;text-align: center;&amp;quot; | Short Description&lt;br /&gt;
|-&lt;br /&gt;
| rowspan=&amp;quot;4&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | 11&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | Treadmill (flat)&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | Walk &amp;amp; run&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | 4km/hr - 8km/hr; increasing in steps&lt;br /&gt;
 of 0.4km/hr every minute&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | 10 min&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | Start walking and switch to &lt;br /&gt;
running at self-selected speed&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | Treadmill (slope)&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | Walk&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | Self-selected&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | 12 min&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | Treadmill is set to (5, 0, 10, 0, 15, 0) degree&lt;br /&gt;
inclinations with 2 mins at each angle&lt;br /&gt;
|-&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Indoor flat space&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Walk &amp;amp; run&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Self-selected&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | 6 min&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Start walking and switch &lt;br /&gt;
to running after 3 mins&lt;br /&gt;
|-&lt;br /&gt;
 |&lt;br /&gt;
|-&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | 9&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Outdoor street&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Walk &amp;amp; run&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Self-selected&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | 6 min&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Start walking and switch &lt;br /&gt;
to running after 3 mins&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
|}&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
==&amp;#039;&amp;#039;&amp;#039;Explanation of the database files&amp;#039;&amp;#039;&amp;#039; (download link below)==&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
=== 1. Activity Timings === &lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
There are two matrices provided, namely, &amp;#039;&amp;#039;&amp;#039;Indoor Experiment timings&amp;#039;&amp;#039;&amp;#039; and  &amp;#039;&amp;#039;&amp;#039;Outdoor Experiment timings&amp;#039;&amp;#039;&amp;#039; which contain the timing information of the activities and are included in a folder called Activity Timings.zip: &lt;br /&gt;
&lt;br /&gt;
[[File:ActivityTimingsGraph.png|right|thumb|500px|caption|&amp;quot;Example of how to use the sample numbers given in Indoor Experiment Timings matrix to extract desired activity data. This figure shows the entire Indoor data collected from accelerometer positioned at Right Ankle for Subject 5. &amp;quot;]]&lt;br /&gt;
&lt;br /&gt;
* &amp;#039;&amp;#039;&amp;#039;Indoor Experiment Timings&amp;#039;&amp;#039;&amp;#039;: This 11(rows) x 8(columns) matrix consists of &amp;#039;&amp;#039;&amp;#039;sample numbers&amp;#039;&amp;#039;&amp;#039; corresponding to the start and end of an activity, for a given subject, for the &amp;#039;&amp;#039;&amp;#039;Indoor Experiments&amp;#039;&amp;#039;&amp;#039;, i.e. &lt;br /&gt;
**Treadmill (flat)&lt;br /&gt;
**Treadmill (slope) &lt;br /&gt;
**Indoor flat space. &lt;br /&gt;
The figure below explains how to extract the sample nos. corresponding to an activity for a given subject, from the Indoor Experiment timings matrix.  &lt;br /&gt;
&lt;br /&gt;
[[File:Indoor Table.png|700px]]&lt;br /&gt;
&lt;br /&gt;
* &amp;#039;&amp;#039;&amp;#039;Outdoor Experiment Timings&amp;#039;&amp;#039;&amp;#039;: This 9(rows) x 3(columns) matrix consists of &amp;#039;&amp;#039;&amp;#039;sample numbers&amp;#039;&amp;#039;&amp;#039; corresponding to the start and end of an activity, for a given subject, for the &amp;#039;&amp;#039;&amp;#039;Outdoor Street Experiments&amp;#039;&amp;#039;&amp;#039;. The figure below explains how to extract the sample nos. corresponding to an activity for a given subject, from the Outdoor Experiment timings matrix.&lt;br /&gt;
&lt;br /&gt;
[[File:Outdoor Table.png|800px]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
=== 2. Subject Data === &lt;br /&gt;
&lt;br /&gt;
[[File:AccXYZ.png|right|thumb|400px|caption|&amp;quot;Figure 2: Example of the accelerometer data from the X, Y and Z axis for Sub5_RF&amp;quot;]]&lt;br /&gt;
&lt;br /&gt;
The Subject data files are provided in two formats, namely, .mat format (&amp;#039;&amp;#039;&amp;#039;Subject Data_mat format.zip&amp;#039;&amp;#039;&amp;#039;) and .txt format &amp;#039;&amp;#039;&amp;#039;Subject Data_txt format).zip&amp;#039;&amp;#039;&amp;#039;. The naming convention of the files is: Sub&amp;lt;number&amp;gt;_&amp;lt;position&amp;gt;, where:&lt;br /&gt;
*&amp;lt;number&amp;gt;: stands for Subject number and ranges from 1 to 20. &lt;br /&gt;
*&amp;lt;position&amp;gt;: stands for the position of the accelerometer on the body as shown in Figure 1. The positions are:&lt;br /&gt;
**LF - Left Ankle&lt;br /&gt;
**RF - Right Ankle&lt;br /&gt;
**Wrist&lt;br /&gt;
**Waist&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
For each Subject file, eg. Sub5_RF, the accelerometer data from the 3 axis accelerometer is stored in 3 columns (separated using comma in the .txt files), each named as: &lt;br /&gt;
*accX - data from X - axis&lt;br /&gt;
*accY - data from Y - axis&lt;br /&gt;
*accZ - data from Z - axis&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
===3. Ground Truth===&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
The Ground Truth is provided as a 11x7 structure that gives the timing of the Heel-Strike and Toe-Off events extracted from the FSR signals. This timing information is provided in terms of sample numbers and is relative to each activity. The structure consists of 7 fields that have &amp;#039;&amp;#039;&amp;#039;already been segregated&amp;#039;&amp;#039;&amp;#039; using the Indoor Experiment Timings and Outdoor Experiment Timings explained above: &lt;br /&gt;
*treadWalk       - treadmill (flat) walk &lt;br /&gt;
&lt;br /&gt;
*treadIncline    - treadmill (slope) walk&lt;br /&gt;
&lt;br /&gt;
*treadWalknRun   - treadmill (flat) walk &amp;amp; run&lt;br /&gt;
&lt;br /&gt;
*indoorWalk      - indoor (flat space) walk&lt;br /&gt;
&lt;br /&gt;
*indoorWalknRun  - indoor (flat space) walk &amp;amp; run&lt;br /&gt;
&lt;br /&gt;
*outdoorWalk     - outdoor street walk&lt;br /&gt;
&lt;br /&gt;
*outdoorWalknRun - outdoor street walk &amp;amp; run &lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
Each row of the structure represents a Subject. For Indoor activities, Row 1 -&amp;gt; Subject1, ..., Row 11 -&amp;gt; Subject 11 and so on. For outdoor activities, Row 1 -&amp;gt; Subject 12,..., Row 9 -&amp;gt; Subject 20. As an example: GroundTruth(1).treadWalk provides the ground truth data for Subject 1 for treadmill (flat) walk. This is a structure with fields: &lt;br /&gt;
* SubIdx - Subject number. Ranges from Sub1 - Sub20&lt;br /&gt;
&lt;br /&gt;
* LF_HS - Sample numbers of Heel-Strike event from Left Foot FSR signal.&lt;br /&gt;
&lt;br /&gt;
* LF_TO - Sample numbers of Toe-Off event from Left Foot FSR signal.&lt;br /&gt;
&lt;br /&gt;
* RF_HS - Sample numbers of Heel-Strike event  from Right Foot FSR signal. &lt;br /&gt;
&lt;br /&gt;
* RF_TO - Sample numbers of Toe-Off event from Right Foot FSR signal.&lt;br /&gt;
&lt;br /&gt;
[[File:GroundTruthStructure.png|1200px]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
=== 4. mainScript.m === &lt;br /&gt;
&lt;br /&gt;
This MATLAB script is an example on how to:&lt;br /&gt;
&lt;br /&gt;
* Read the accelerometer data of a subject for an activity &amp;amp; position&lt;br /&gt;
&lt;br /&gt;
* Read the Ground Truth Heel-Strike and Toe-Off gait events for the same&lt;br /&gt;
&lt;br /&gt;
* Plot all the signals&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
----&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 160%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;How to get the datasets?&amp;#039;&amp;#039;&amp;#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
&lt;br /&gt;
----------------------------------------------------------------------------------------------------------------------------------&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 110%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;1. Link to the Release Agreement and fill Registration Form:&amp;#039;&amp;#039;&amp;#039;  [https://wiki.hh.se/caisr/index.php/Gait_database:_Release_agreement]&amp;lt;/div&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 110%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;2. Link to download the datasets:&amp;#039;&amp;#039;&amp;#039;  Will be sent by Email after Registration Procedure is completed &amp;lt;/div&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 160%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;References&amp;#039;&amp;#039;&amp;#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
----------------------------------------------------&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 120%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;Please remember to include an appropriate citation to acknowledge the use of the database in all documents and papers that uses the MAREA Gait Database:&amp;#039;&amp;#039;&amp;#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
1. Siddhartha Khandelwal, Nicholas Wickström, Evaluation of the performance of accelerometer-based gait event detection algorithms in different real-world scenarios using the MAREA gait database, Gait &amp;amp; Posture, Volume 51, January 2017, Pages 84-90, ISSN 0966-6362, http://dx.doi.org/10.1016/j.gaitpost.2016.09.023.&lt;br /&gt;
&lt;br /&gt;
Sciencedirect: [http://www.sciencedirect.com/science/article/pii/S0966636216305859]&lt;br /&gt;
Diva: [http://hh.diva-portal.org/smash/record.jsf?searchId=1&amp;amp;pid=diva2%3A999938&amp;amp;dswid=1416]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 120%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;You may also want to read:&amp;#039;&amp;#039;&amp;#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
2. Siddhartha Khandelwal; Nicholas Wickström, &amp;quot;Gait Event Detection in Real-World Environment for Long-Term Applications: Incorporating Domain Knowledge into Time-Frequency Analysis,&amp;quot; in IEEE Transactions on Neural Systems and Rehabilitation Engineering , vol. 24, no. 12, pp.1363-1372, 2016 &lt;br /&gt;
&lt;br /&gt;
IEEE url: [http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7423805&amp;amp;newsearch=true&amp;amp;queryText=siddhartha%20khandelwal]&lt;br /&gt;
DiVA url: [http://hh.diva-portal.org/smash/record.jsf?searchId=1&amp;amp;pid=diva2:909015]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
3. Siddhartha Khandelwal, Nicholas Wickström, &amp;quot;Novel methodology for estimating Initial Contact events from accelerometers positioned at different body locations&amp;quot;, in Gait &amp;amp; Posture, Volume 59, Pages 278–285, Jan 2018.&lt;br /&gt;
&lt;br /&gt;
DOI: http://dx.doi.org/10.1016/j.gaitpost.2017.07.030&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
4. Siddhartha Khandelwal; Nicholas Wickström, &amp;quot;Identification of Gait Events using Expert Knowledge and Continuous Wavelet Transform Analysis&amp;quot;, in BIOSIGNALS 2014, 7th International Conference on Bio-inspired Systems and Signal Processing, Angers, France, March 3-6, 2014 &lt;br /&gt;
&lt;br /&gt;
DiVA url: [http://hh.diva-portal.org/smash/record.jsf?searchId=1&amp;amp;pid=diva2:688909]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
5. Siddhartha Khandelwal, Nicholas Wickström, &amp;quot;Detecting Gait Events from Outdoor Accelerometer Data for Long-term and Continuous Monitoring Applications&amp;quot; in 13th International Symposium on 3D Analysis of Human Movement (3D-AHM 2014), 14–17 July, 2014, Lausanne, Switzerland.&lt;br /&gt;
&lt;br /&gt;
DiVA url: [http://www.diva-portal.org/smash/record.jsf?pid=diva2:735193]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 160%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;Created By&amp;#039;&amp;#039;&amp;#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
----------------------------------------------------&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
[http://islab.hh.se/mediawiki/Siddhartha_Khandelwal Siddhartha Khandelwal]&lt;br /&gt;
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----&lt;/div&gt;</summary>
		<author><name>Siddhartha</name></author>
	</entry>
	<entry>
		<id>https://mw.hh.se/caisr/index.php?title=Siddhartha_Khandelwal&amp;diff=4812</id>
		<title>Siddhartha Khandelwal</title>
		<link rel="alternate" type="text/html" href="https://mw.hh.se/caisr/index.php?title=Siddhartha_Khandelwal&amp;diff=4812"/>
		<updated>2021-03-01T16:00:26Z</updated>

		<summary type="html">&lt;p&gt;Siddhartha: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;{{Person&lt;br /&gt;
|Family Name=Khandelwal&lt;br /&gt;
|Given Name=Siddhartha&lt;br /&gt;
|Title=PhD&lt;br /&gt;
|Email=siddhartha.khandelwal@hh.se&lt;br /&gt;
|Image=Sid.jpg&lt;br /&gt;
|Country=Sweden&lt;br /&gt;
|Subject=Human Motion Analysis using Wearable Sensors&lt;br /&gt;
}}&lt;br /&gt;
{{AssignProjects&lt;br /&gt;
|project=HMC2&lt;br /&gt;
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{{AssignSubjectAreas&lt;br /&gt;
|SubjectArea=Signal Analysis&lt;br /&gt;
}}&lt;br /&gt;
{{AssignApplicationAreas&lt;br /&gt;
|ApplicationArea=Health Technology&lt;br /&gt;
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{{AssignExaminer&lt;br /&gt;
|Examiner=Matlab with Applications (7.5 credits)&lt;br /&gt;
}}&lt;br /&gt;
{{AssignExaminer&lt;br /&gt;
|Examiner=Robotic Manipulators (Teaching Assistant, 7.5 credits)&lt;br /&gt;
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[[Category:alumni]]&lt;br /&gt;
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== About Siddhartha ==&lt;br /&gt;
&lt;br /&gt;
Currently, he is the CEO of VectorizeMove: a start-up aimed at bringing the benefits of advanced gait analysis to peoples&amp;#039; everyday lives. Read more at: www.vectorizemove.com&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== Siddhartha elsewhere == &lt;br /&gt;
&lt;br /&gt;
[http://scholar.google.se/citations?user=7uLMICoAAAAJ&amp;amp;hl=en Google Scholar] &lt;br /&gt;
&lt;br /&gt;
[https://www.linkedin.com/in/siddharthakhandelwal/ LinkedIn] &lt;br /&gt;
&lt;br /&gt;
[http://www.researchgate.net/profile/Siddhartha_Khandelwal/ ResearchGate]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== MAREA Gait Database ==&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
MAREA gait database:  https://wiki.hh.se/caisr/index.php/Gait_database &lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
{{PublicationsList}}&lt;/div&gt;</summary>
		<author><name>Siddhartha</name></author>
	</entry>
	<entry>
		<id>https://mw.hh.se/caisr/index.php?title=Siddhartha_Khandelwal&amp;diff=4543</id>
		<title>Siddhartha Khandelwal</title>
		<link rel="alternate" type="text/html" href="https://mw.hh.se/caisr/index.php?title=Siddhartha_Khandelwal&amp;diff=4543"/>
		<updated>2020-02-24T08:52:04Z</updated>

		<summary type="html">&lt;p&gt;Siddhartha: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;{{Person&lt;br /&gt;
|Family Name=Khandelwal&lt;br /&gt;
|Given Name=Siddhartha&lt;br /&gt;
|Title=PhD&lt;br /&gt;
|Phone=+46-35-16-7667&lt;br /&gt;
|Position=&lt;br /&gt;
|Email=siddhartha.khandelwal@hh.se&lt;br /&gt;
|Image=Sid.jpg&lt;br /&gt;
|Country=Sweden&lt;br /&gt;
|Office=E522&lt;br /&gt;
|url= &lt;br /&gt;
|Subject=Human Motion Analysis using Wearable Sensors&lt;br /&gt;
}}&lt;br /&gt;
{{AssignProjects&lt;br /&gt;
|project=HMC2&lt;br /&gt;
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{{AssignSubjectAreas&lt;br /&gt;
|SubjectArea= Signal Analysis&lt;br /&gt;
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{{AssignApplicationAreas&lt;br /&gt;
|ApplicationArea= Health Technology&lt;br /&gt;
}}&lt;br /&gt;
{{AssignExaminer&lt;br /&gt;
|Examiner=Matlab with Applications (7.5 credits)&lt;br /&gt;
}}&lt;br /&gt;
{{AssignExaminer&lt;br /&gt;
|Examiner=Robotic Manipulators (Teaching Assistant, 7.5 credits)&lt;br /&gt;
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== About Siddhartha ==&lt;br /&gt;
&lt;br /&gt;
After finishing his Bachelors in Electronics from VIT University, India and TU Dresden, Germany (Bachelor Thesis), he worked for almost an year in a Robotics Start-up called ThinkLabs at IIT, Mumbai, India. Then he received the Erasmus-Mundus scholarship EMARO to pursue Masters in Robotics at Warsaw University of Technology, Poland and Ecole Centrale Nantes, France. In March 2018, he finished his PhD at Halmstad University on Gait Event Detection in the Real World. &lt;br /&gt;
&lt;br /&gt;
Currently, he is the founder and CEO of VectorizeMove: a start-up aimed at bringing the benefits of advanced gait analysis to peoples&amp;#039; everyday lives. Read more at: www.vectorizemove.com&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== Siddhartha elsewhere == &lt;br /&gt;
&lt;br /&gt;
[http://scholar.google.se/citations?user=7uLMICoAAAAJ&amp;amp;hl=en Google Scholar] &lt;br /&gt;
&lt;br /&gt;
[https://www.linkedin.com/in/siddharthakhandelwal/ LinkedIn] &lt;br /&gt;
&lt;br /&gt;
[http://www.researchgate.net/profile/Siddhartha_Khandelwal/ ResearchGate]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== MAREA Gait Database ==&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
MAREA gait database:  http://islab.hh.se/mediawiki/Gait_database &lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
{{PublicationsList}}&lt;/div&gt;</summary>
		<author><name>Siddhartha</name></author>
	</entry>
	<entry>
		<id>https://mw.hh.se/caisr/index.php?title=Gait_events&amp;diff=4077</id>
		<title>Gait events</title>
		<link rel="alternate" type="text/html" href="https://mw.hh.se/caisr/index.php?title=Gait_events&amp;diff=4077"/>
		<updated>2018-10-21T09:52:37Z</updated>

		<summary type="html">&lt;p&gt;Siddhartha: Replaced content with &amp;quot;This page no longer exists&amp;quot;&lt;/p&gt;
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&lt;div&gt;This page no longer exists&lt;/div&gt;</summary>
		<author><name>Siddhartha</name></author>
	</entry>
	<entry>
		<id>https://mw.hh.se/caisr/index.php?title=Siddhartha_Khandelwal&amp;diff=3904</id>
		<title>Siddhartha Khandelwal</title>
		<link rel="alternate" type="text/html" href="https://mw.hh.se/caisr/index.php?title=Siddhartha_Khandelwal&amp;diff=3904"/>
		<updated>2018-03-14T21:43:40Z</updated>

		<summary type="html">&lt;p&gt;Siddhartha: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;{{Person&lt;br /&gt;
|Family Name=Khandelwal&lt;br /&gt;
|Given Name=Siddhartha&lt;br /&gt;
|Title=PhD&lt;br /&gt;
|Phone=+46-35-16-7667&lt;br /&gt;
|Position=&lt;br /&gt;
|Email=siddhartha.khandelwal@hh.se&lt;br /&gt;
|Image=Sid.jpg&lt;br /&gt;
|Country=Sweden&lt;br /&gt;
|Office=E522&lt;br /&gt;
|url= &lt;br /&gt;
|Subject=Human Motion Analysis using Wearable Sensors&lt;br /&gt;
}}&lt;br /&gt;
{{AssignProjects&lt;br /&gt;
|project=HMC2&lt;br /&gt;
}}&lt;br /&gt;
{{AssignSubjectAreas&lt;br /&gt;
|SubjectArea= Signal Analysis&lt;br /&gt;
}}&lt;br /&gt;
{{AssignApplicationAreas&lt;br /&gt;
|ApplicationArea= Health Technology&lt;br /&gt;
}}&lt;br /&gt;
{{AssignExaminer&lt;br /&gt;
|Examiner=Matlab with Applications (7.5 credits)&lt;br /&gt;
}}&lt;br /&gt;
{{AssignExaminer&lt;br /&gt;
|Examiner=Robotic Manipulators (Teaching Assistant, 7.5 credits)&lt;br /&gt;
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== About Siddhartha ==&lt;br /&gt;
&lt;br /&gt;
After finishing his Bachelors in Electronics from VIT University, India[http://www.vit.ac.in/] and TU Dresden, Germany (Bachelor Thesis)[http://tu-dresden.de/en], he worked for almost an year in a Robotics Start-up called ThinkLabs at IIT, Bombay (India)[http://www.thinklabs.in/]. Then he received the Erasmus-Mundus scholarship &amp;quot;EMARO&amp;quot; [http://emaro.irccyn.ec-nantes.fr/] to pursue Masters in Robotics at Warsaw University of Technology, Poland [http://www.pw.edu.pl/engpw] and Ecole Centrale Nantes, France [http://www.ec-nantes.fr/version-anglaise/]. In March 2018, he finished his PhD at Halmstad University on Gait Event Detection in the Real World. &lt;br /&gt;
&lt;br /&gt;
His current research involves quantitative and qualitative analysis of human motion using wearable sensors (especially accelerometers) with focus on gait analysis, involving:&lt;br /&gt;
&lt;br /&gt;
* Gait event detection in real-world environments for long-term and continuous monitoring applications&lt;br /&gt;
* Activity classification and characterization of gait using wearable sensors &lt;br /&gt;
* Early prediction and continuous monitoring of neuro-physiological diseases such as Parkinson, cerebral palsy, etc&lt;br /&gt;
* Analyzing the performance and technique of elite athletes with special focus on bicycling&lt;br /&gt;
* Prediction of energy expenditure using wearable sensors&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== Siddhartha elsewhere == &lt;br /&gt;
&lt;br /&gt;
[http://scholar.google.se/citations?user=7uLMICoAAAAJ&amp;amp;hl=en Google Scholar] &lt;br /&gt;
&lt;br /&gt;
[http://www.linkedin.com/in/siddhartha-khandelwal-363b7718/ LinkedIn] &lt;br /&gt;
&lt;br /&gt;
[http://www.researchgate.net/profile/Siddhartha_Khandelwal/ ResearchGate]&lt;br /&gt;
&lt;br /&gt;
[http://yourshot.nationalgeographic.com/profile/262109/ YourShot National Geographic]&lt;br /&gt;
&lt;br /&gt;
== MAREA Gait Database ==&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
MAREA gait database:  http://islab.hh.se/mediawiki/Gait_database &lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
{{PublicationsList}}&lt;/div&gt;</summary>
		<author><name>Siddhartha</name></author>
	</entry>
	<entry>
		<id>https://mw.hh.se/caisr/index.php?title=Siddhartha_Khandelwal&amp;diff=3903</id>
		<title>Siddhartha Khandelwal</title>
		<link rel="alternate" type="text/html" href="https://mw.hh.se/caisr/index.php?title=Siddhartha_Khandelwal&amp;diff=3903"/>
		<updated>2018-03-14T21:43:10Z</updated>

		<summary type="html">&lt;p&gt;Siddhartha: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;{{Person&lt;br /&gt;
|Family Name=Khandelwal&lt;br /&gt;
|Given Name=Siddhartha&lt;br /&gt;
|Title=PhD&lt;br /&gt;
|Phone=+46-35-16-7667&lt;br /&gt;
|Position=Research Engineer&lt;br /&gt;
|Email=siddhartha.khandelwal@hh.se&lt;br /&gt;
|Image=Sid.jpg&lt;br /&gt;
|Country=Sweden&lt;br /&gt;
|Office=E522&lt;br /&gt;
|url= &lt;br /&gt;
|Subject=Human Motion Analysis using Wearable Sensors&lt;br /&gt;
}}&lt;br /&gt;
{{AssignProjects&lt;br /&gt;
|project=HMC2&lt;br /&gt;
}}&lt;br /&gt;
{{AssignSubjectAreas&lt;br /&gt;
|SubjectArea= Signal Analysis&lt;br /&gt;
}}&lt;br /&gt;
{{AssignApplicationAreas&lt;br /&gt;
|ApplicationArea= Health Technology&lt;br /&gt;
}}&lt;br /&gt;
{{AssignExaminer&lt;br /&gt;
|Examiner=Matlab with Applications (7.5 credits)&lt;br /&gt;
}}&lt;br /&gt;
{{AssignExaminer&lt;br /&gt;
|Examiner=Robotic Manipulators (Teaching Assistant, 7.5 credits)&lt;br /&gt;
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[[Category:Staff]]&lt;br /&gt;
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&lt;br /&gt;
&lt;br /&gt;
== About Siddhartha ==&lt;br /&gt;
&lt;br /&gt;
After finishing his Bachelors in Electronics from VIT University, India[http://www.vit.ac.in/] and TU Dresden, Germany (Bachelor Thesis)[http://tu-dresden.de/en], he worked for almost an year in a Robotics Start-up called ThinkLabs at IIT, Bombay (India)[http://www.thinklabs.in/]. Then he received the Erasmus-Mundus scholarship &amp;quot;EMARO&amp;quot; [http://emaro.irccyn.ec-nantes.fr/] to pursue Masters in Robotics at Warsaw University of Technology, Poland [http://www.pw.edu.pl/engpw] and Ecole Centrale Nantes, France [http://www.ec-nantes.fr/version-anglaise/]. In March 2018, he finished his PhD at Halmstad University on Gait Event Detection in the Real World. &lt;br /&gt;
&lt;br /&gt;
His current research involves quantitative and qualitative analysis of human motion using wearable sensors (especially accelerometers) with focus on gait analysis, involving:&lt;br /&gt;
&lt;br /&gt;
* Gait event detection in real-world environments for long-term and continuous monitoring applications&lt;br /&gt;
* Activity classification and characterization of gait using wearable sensors &lt;br /&gt;
* Early prediction and continuous monitoring of neuro-physiological diseases such as Parkinson, cerebral palsy, etc&lt;br /&gt;
* Analyzing the performance and technique of elite athletes with special focus on bicycling&lt;br /&gt;
* Prediction of energy expenditure using wearable sensors&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== Siddhartha elsewhere == &lt;br /&gt;
&lt;br /&gt;
[http://scholar.google.se/citations?user=7uLMICoAAAAJ&amp;amp;hl=en Google Scholar] &lt;br /&gt;
&lt;br /&gt;
[http://www.linkedin.com/in/siddhartha-khandelwal-363b7718/ LinkedIn] &lt;br /&gt;
&lt;br /&gt;
[http://www.researchgate.net/profile/Siddhartha_Khandelwal/ ResearchGate]&lt;br /&gt;
&lt;br /&gt;
[http://yourshot.nationalgeographic.com/profile/262109/ YourShot National Geographic]&lt;br /&gt;
&lt;br /&gt;
== MAREA Gait Database ==&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
MAREA gait database:  http://islab.hh.se/mediawiki/Gait_database &lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
{{PublicationsList}}&lt;/div&gt;</summary>
		<author><name>Siddhartha</name></author>
	</entry>
	<entry>
		<id>https://mw.hh.se/caisr/index.php?title=Siddhartha_Khandelwal&amp;diff=3901</id>
		<title>Siddhartha Khandelwal</title>
		<link rel="alternate" type="text/html" href="https://mw.hh.se/caisr/index.php?title=Siddhartha_Khandelwal&amp;diff=3901"/>
		<updated>2018-03-10T11:28:31Z</updated>

		<summary type="html">&lt;p&gt;Siddhartha: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;{{Person&lt;br /&gt;
|Family Name=Khandelwal&lt;br /&gt;
|Given Name=Siddhartha&lt;br /&gt;
|Title=M.Sc&lt;br /&gt;
|Phone=+46-35-16-7667&lt;br /&gt;
|Position=PhD Candidate&lt;br /&gt;
|Email=siddhartha.khandelwal@hh.se&lt;br /&gt;
|Image=Sid.jpg&lt;br /&gt;
|Country=Sweden&lt;br /&gt;
|Office=E522&lt;br /&gt;
|url= &lt;br /&gt;
|Subject=Human Motion Analysis using Wearable Sensors&lt;br /&gt;
}}&lt;br /&gt;
{{AssignProjects&lt;br /&gt;
|project=HMC2&lt;br /&gt;
}}&lt;br /&gt;
{{AssignSubjectAreas&lt;br /&gt;
|SubjectArea= Signal Analysis&lt;br /&gt;
}}&lt;br /&gt;
{{AssignApplicationAreas&lt;br /&gt;
|ApplicationArea= Health Technology&lt;br /&gt;
}}&lt;br /&gt;
{{AssignExaminer&lt;br /&gt;
|Examiner=Matlab with Applications (7.5 credits)&lt;br /&gt;
}}&lt;br /&gt;
{{AssignExaminer&lt;br /&gt;
|Examiner=Robotic Manipulators (Teaching Assistant, 7.5 credits)&lt;br /&gt;
}}&lt;br /&gt;
&lt;br /&gt;
[[Category:Staff]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;!--Remove or add comments --&amp;gt;&lt;br /&gt;
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&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== About Siddhartha ==&lt;br /&gt;
&lt;br /&gt;
After finishing his Bachelors in Electronics from VIT University, India[http://www.vit.ac.in/] and TU Dresden, Germany (Bachelor Thesis)[http://tu-dresden.de/en], he worked for almost an year in a Robotics Start-up called ThinkLabs at IIT, Bombay (India)[http://www.thinklabs.in/]. Then he received the Erasmus-Mundus scholarship &amp;quot;EMARO&amp;quot; [http://emaro.irccyn.ec-nantes.fr/] to pursue Masters in Robotics at Warsaw University of Technology, Poland [http://www.pw.edu.pl/engpw] and Ecole Centrale Nantes, France [http://www.ec-nantes.fr/version-anglaise/]. In Nov 2012, he joined CAISR at Halmstad University to pursue his PhD on Human Motion Analysis using Wearable Sensors. His current research involves quantitative and qualitative analysis of human motion using wearable sensors (especially accelerometers) with focus on gait analysis, involving:&lt;br /&gt;
&lt;br /&gt;
* Gait event detection in real-world environments for long-term and continuous monitoring applications&lt;br /&gt;
* Activity classification and characterization of gait using wearable sensors &lt;br /&gt;
* Early prediction and continuous monitoring of neuro-physiological diseases such as Parkinson, cerebral palsy, etc&lt;br /&gt;
* Analyzing the performance and technique of elite athletes with special focus on bicycling&lt;br /&gt;
* Prediction of energy expenditure using wearable sensors&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== Siddhartha elsewhere == &lt;br /&gt;
&lt;br /&gt;
[http://scholar.google.se/citations?user=7uLMICoAAAAJ&amp;amp;hl=en Google Scholar] &lt;br /&gt;
&lt;br /&gt;
[http://www.linkedin.com/in/siddhartha-khandelwal-363b7718/ LinkedIn] &lt;br /&gt;
&lt;br /&gt;
[http://www.researchgate.net/profile/Siddhartha_Khandelwal/ ResearchGate]&lt;br /&gt;
&lt;br /&gt;
[http://yourshot.nationalgeographic.com/profile/262109/ YourShot National Geographic]&lt;br /&gt;
&lt;br /&gt;
== MAREA Gait Database ==&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
MAREA gait database:  http://islab.hh.se/mediawiki/Gait_database &lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
{{PublicationsList}}&lt;/div&gt;</summary>
		<author><name>Siddhartha</name></author>
	</entry>
	<entry>
		<id>https://mw.hh.se/caisr/index.php?title=Siddhartha_Khandelwal&amp;diff=3900</id>
		<title>Siddhartha Khandelwal</title>
		<link rel="alternate" type="text/html" href="https://mw.hh.se/caisr/index.php?title=Siddhartha_Khandelwal&amp;diff=3900"/>
		<updated>2018-03-10T11:28:00Z</updated>

		<summary type="html">&lt;p&gt;Siddhartha: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;{{Person&lt;br /&gt;
|Family Name=Khandelwal&lt;br /&gt;
|Given Name=Siddhartha&lt;br /&gt;
|Title=M.Sc&lt;br /&gt;
|Phone=+46-35-16-7667&lt;br /&gt;
|Position=PhD Candidate&lt;br /&gt;
|Email=siddhartha.khandelwal@hh.se&lt;br /&gt;
|Image=Sid.jpg&lt;br /&gt;
|Country=Sweden&lt;br /&gt;
|Office=E522&lt;br /&gt;
|url= &lt;br /&gt;
|Subject=Human Motion Analysis using Wearable Sensors&lt;br /&gt;
}}&lt;br /&gt;
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|project=HMC2&lt;br /&gt;
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|ApplicationArea= Health Technology&lt;br /&gt;
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|Examiner=Matlab with Applications (7.5 credits)&lt;br /&gt;
}}&lt;br /&gt;
{{AssignExaminer&lt;br /&gt;
|Examiner=Robotic Manipulators (Teaching Assistant, 7.5 credits)&lt;br /&gt;
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&lt;br /&gt;
&lt;br /&gt;
== About Siddhartha ==&lt;br /&gt;
&lt;br /&gt;
After finishing his Bachelors in Electronics from VIT University, India[http://www.vit.ac.in/] and TU Dresden, Germany (Bachelor Thesis)[http://tu-dresden.de/en], he worked for almost an year in a Robotics Start-up called ThinkLabs at IIT, Bombay (India)[http://www.thinklabs.in/]. Then he received the Erasmus-Mundus scholarship &amp;quot;EMARO&amp;quot; [http://emaro.irccyn.ec-nantes.fr/] to pursue Masters in Robotics at Warsaw University of Technology, Poland [http://www.pw.edu.pl/engpw] and Ecole Centrale Nantes, France [http://www.ec-nantes.fr/version-anglaise/]. In Nov 2012, he joined CAISR at Halmstad University to pursue his PhD on Human Motion Analysis using Wearable Sensors. His current research involves quantitative and qualitative analysis of human motion using wearable sensors (especially accelerometers) with focus on gait analysis, involving:&lt;br /&gt;
&lt;br /&gt;
* Gait event detection in real-world environments for long-term and continuous monitoring applications&lt;br /&gt;
* Activity classification and characterization of gait using wearable sensors &lt;br /&gt;
* Early prediction and continuous monitoring of neuro-physiological diseases such as Parkinson, cerebral palsy, etc&lt;br /&gt;
* Analyzing the performance and technique of elite athletes with special focus on bicycling&lt;br /&gt;
* Prediction of energy expenditure using wearable sensors&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== Siddhartha elsewhere == &lt;br /&gt;
&lt;br /&gt;
[https://scholar.google.se/citations?user=7uLMICoAAAAJ&amp;amp;hl=en Google Scholar] &lt;br /&gt;
&lt;br /&gt;
[http://www.linkedin.com/in/siddhartha-khandelwal-363b7718/ LinkedIn] &lt;br /&gt;
&lt;br /&gt;
[http://www.researchgate.net/profile/Siddhartha_Khandelwal/ ResearchGate]&lt;br /&gt;
&lt;br /&gt;
[http://yourshot.nationalgeographic.com/profile/262109/ YourShot National Geographic]&lt;br /&gt;
&lt;br /&gt;
== MAREA Gait Database ==&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
MAREA gait database:  http://islab.hh.se/mediawiki/Gait_database &lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
{{PublicationsList}}&lt;/div&gt;</summary>
		<author><name>Siddhartha</name></author>
	</entry>
	<entry>
		<id>https://mw.hh.se/caisr/index.php?title=Gait_database&amp;diff=3850</id>
		<title>Gait database</title>
		<link rel="alternate" type="text/html" href="https://mw.hh.se/caisr/index.php?title=Gait_database&amp;diff=3850"/>
		<updated>2018-01-15T16:45:57Z</updated>

		<summary type="html">&lt;p&gt;Siddhartha: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== MAREA (&amp;#039;&amp;#039;&amp;#039;M&amp;#039;&amp;#039;&amp;#039;ovement &amp;#039;&amp;#039;&amp;#039;A&amp;#039;&amp;#039;&amp;#039;nalysis in &amp;#039;&amp;#039;&amp;#039;R&amp;#039;&amp;#039;&amp;#039;eal-world &amp;#039;&amp;#039;&amp;#039;E&amp;#039;&amp;#039;&amp;#039;nvironments using &amp;#039;&amp;#039;&amp;#039;A&amp;#039;&amp;#039;&amp;#039;ccelerometers) Gait Database ==&lt;br /&gt;
&lt;br /&gt;
[[File:accPlacement.jpg|right|thumb|200px|caption|&amp;quot;Figure 1: Position and orientation of each accelerometer at the beginning of each experiment. The accelerometer at the right ankle is attached arbitrarily without any pre-defined orientation. The FSRs are instrumented into the shoe soles to collect the ground truth. The data from all sensors is sampled at a frequency of 128 Hz.&amp;quot;]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
The &amp;#039;&amp;#039;&amp;#039;MAREA&amp;#039;&amp;#039;&amp;#039; gait database comprises of gait activities in different real-world environments as shown in the table below. 20 healthy adults (12 males and 8 females, average age: 33.4 +- 7 years, average mass: 73.2 +- 10.9 kg, average height: 172.6 +- 9.5 cm) participated in the study that was approved by the Ethical Review Board of Lund, Sweden. Each subject had a 3-axes Shimmer3 (Shimmer Research, Dublin, Ireland) accelerometer (+- 8g) attached to their waist, left wrist and left and right ankles using elastic bands and velcro straps. Figure 1 shows the position and orientation of each accelerometer at the beginning of each experiment. On the waist, the accelerometer X and Y axes were pointing to the lateral and downward direction, respectively. On the wrist and left ankle, the Z axis was pointing in the lateral direction while the Y axis was pointing downward and was aligned with the limb longitudinal axis. In order to simulate a lesser controlled scenario, the accelerometer on right ankle was positioned such that the Y axis was pointing downward but the Z axis was marginally disturbed such that it was not exactly perpendicular to the sagittal plane. The subjects were provided shoes that were instrumented with piezo-electric force sensitive resistors (FSRs), fixed at the extreme ends of the sole in order to provide the ground truth values for HS and TO. An external expansion board was used to synchronously collect the data from the FSRs on each foot and the respective ankle accelerometer, at a sampling frequency of 128Hz, and stored locally on the Shimmer3 microSD card. Due to the lack of a centralized data logger, the waist accelerometer was not in perfect synchronization with the ankle accelerometer.&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
{| class=&amp;quot;wikitable&amp;quot;&lt;br /&gt;
! style=&amp;quot;text-align: center;&amp;quot; | Subjects&lt;br /&gt;
! style=&amp;quot;text-align: center;&amp;quot; | Environment&lt;br /&gt;
! style=&amp;quot;text-align: center;&amp;quot; | Activity&lt;br /&gt;
! style=&amp;quot;text-align: center;&amp;quot; | Speed&lt;br /&gt;
! style=&amp;quot;text-align: center;&amp;quot; | Duration&lt;br /&gt;
! style=&amp;quot;text-align: center;&amp;quot; | Short Description&lt;br /&gt;
|-&lt;br /&gt;
| rowspan=&amp;quot;4&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | 11&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | Treadmill (flat)&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | Walk &amp;amp; run&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | 4km/hr - 8km/hr; increasing in steps&lt;br /&gt;
 of 0.4km/hr every minute&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | 10 min&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | Start walking and switch to &lt;br /&gt;
running at self-selected speed&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | Treadmill (slope)&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | Walk&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | Self-selected&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | 12 min&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | Treadmill is set to (5, 0, 10, 0, 15, 0) degree&lt;br /&gt;
inclinations with 2 mins at each angle&lt;br /&gt;
|-&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Indoor flat space&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Walk &amp;amp; run&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Self-selected&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | 6 min&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Start walking and switch &lt;br /&gt;
to running after 3 mins&lt;br /&gt;
|-&lt;br /&gt;
 |&lt;br /&gt;
|-&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | 9&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Outdoor street&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Walk &amp;amp; run&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Self-selected&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | 6 min&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Start walking and switch &lt;br /&gt;
to running after 3 mins&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
|}&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
==&amp;#039;&amp;#039;&amp;#039;Explanation of the database files&amp;#039;&amp;#039;&amp;#039; (download link below)==&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
=== 1. Activity Timings === &lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
There are two matrices provided, namely, &amp;#039;&amp;#039;&amp;#039;Indoor Experiment timings&amp;#039;&amp;#039;&amp;#039; and  &amp;#039;&amp;#039;&amp;#039;Outdoor Experiment timings&amp;#039;&amp;#039;&amp;#039; which contain the timing information of the activities and are included in a folder called Activity Timings.zip: &lt;br /&gt;
&lt;br /&gt;
[[File:ActivityTimingsGraph.png|right|thumb|500px|caption|&amp;quot;Example of how to use the sample numbers given in Indoor Experiment Timings matrix to extract desired activity data. This figure shows the entire Indoor data collected from accelerometer positioned at Right Ankle for Subject 5. &amp;quot;]]&lt;br /&gt;
&lt;br /&gt;
* &amp;#039;&amp;#039;&amp;#039;Indoor Experiment Timings&amp;#039;&amp;#039;&amp;#039;: This 11(rows) x 8(columns) matrix consists of &amp;#039;&amp;#039;&amp;#039;sample numbers&amp;#039;&amp;#039;&amp;#039; corresponding to the start and end of an activity, for a given subject, for the &amp;#039;&amp;#039;&amp;#039;Indoor Experiments&amp;#039;&amp;#039;&amp;#039;, i.e. &lt;br /&gt;
**Treadmill (flat)&lt;br /&gt;
**Treadmill (slope) &lt;br /&gt;
**Indoor flat space. &lt;br /&gt;
The figure below explains how to extract the sample nos. corresponding to an activity for a given subject, from the Indoor Experiment timings matrix.  &lt;br /&gt;
&lt;br /&gt;
[[File:Indoor Table.png|700px]]&lt;br /&gt;
&lt;br /&gt;
* &amp;#039;&amp;#039;&amp;#039;Outdoor Experiment Timings&amp;#039;&amp;#039;&amp;#039;: This 9(rows) x 3(columns) matrix consists of &amp;#039;&amp;#039;&amp;#039;sample numbers&amp;#039;&amp;#039;&amp;#039; corresponding to the start and end of an activity, for a given subject, for the &amp;#039;&amp;#039;&amp;#039;Outdoor Street Experiments&amp;#039;&amp;#039;&amp;#039;. The figure below explains how to extract the sample nos. corresponding to an activity for a given subject, from the Outdoor Experiment timings matrix.&lt;br /&gt;
&lt;br /&gt;
[[File:Outdoor Table.png|800px]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
=== 2. Subject Data === &lt;br /&gt;
&lt;br /&gt;
[[File:AccXYZ.png|right|thumb|400px|caption|&amp;quot;Figure 2: Example of the accelerometer data from the X, Y and Z axis for Sub5_RF&amp;quot;]]&lt;br /&gt;
&lt;br /&gt;
The Subject data files are provided in two formats, namely, .mat format (&amp;#039;&amp;#039;&amp;#039;Subject Data_mat format.zip&amp;#039;&amp;#039;&amp;#039;) and .txt format &amp;#039;&amp;#039;&amp;#039;Subject Data_txt format).zip&amp;#039;&amp;#039;&amp;#039;. The naming convention of the files is: Sub&amp;lt;number&amp;gt;_&amp;lt;position&amp;gt;, where:&lt;br /&gt;
*&amp;lt;number&amp;gt;: stands for Subject number and ranges from 1 to 20. &lt;br /&gt;
*&amp;lt;position&amp;gt;: stands for the position of the accelerometer on the body as shown in Figure 1. The positions are:&lt;br /&gt;
**LF - Left Ankle&lt;br /&gt;
**RF - Right Ankle&lt;br /&gt;
**Wrist&lt;br /&gt;
**Waist&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
For each Subject file, eg. Sub5_RF, the accelerometer data from the 3 axis accelerometer is stored in 3 columns (separated using comma in the .txt files), each named as: &lt;br /&gt;
*accX - data from X - axis&lt;br /&gt;
*accY - data from Y - axis&lt;br /&gt;
*accZ - data from Z - axis&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
===3. Ground Truth===&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
The Ground Truth is provided as a 11x7 structure that gives the timing of the Heel-Strike and Toe-Off events extracted from the FSR signals. This timing information is provided in terms of sample numbers and is relative to each activity. The structure consists of 7 fields that have &amp;#039;&amp;#039;&amp;#039;already been segregated&amp;#039;&amp;#039;&amp;#039; using the Indoor Experiment Timings and Outdoor Experiment Timings explained above: &lt;br /&gt;
*treadWalk       - treadmill (flat) walk &lt;br /&gt;
&lt;br /&gt;
*treadIncline    - treadmill (slope) walk&lt;br /&gt;
&lt;br /&gt;
*treadWalknRun   - treadmill (flat) walk &amp;amp; run&lt;br /&gt;
&lt;br /&gt;
*indoorWalk      - indoor (flat space) walk&lt;br /&gt;
&lt;br /&gt;
*indoorWalknRun  - indoor (flat space) walk &amp;amp; run&lt;br /&gt;
&lt;br /&gt;
*outdoorWalk     - outdoor street walk&lt;br /&gt;
&lt;br /&gt;
*outdoorWalknRun - outdoor street walk &amp;amp; run &lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
Each row of the structure represents a Subject. For Indoor activities, Row 1 -&amp;gt; Subject1, ..., Row 11 -&amp;gt; Subject 11 and so on. For outdoor activities, Row 1 -&amp;gt; Subject 12,..., Row 9 -&amp;gt; Subject 20. As an example: GroundTruth(1).treadWalk provides the ground truth data for Subject 1 for treadmill (flat) walk. This is a structure with fields: &lt;br /&gt;
* SubIdx - Subject number. Ranges from Sub1 - Sub20&lt;br /&gt;
&lt;br /&gt;
* LF_HS - Sample numbers of Heel-Strike event from Left Foot FSR signal.&lt;br /&gt;
&lt;br /&gt;
* LF_TO - Sample numbers of Toe-Off event from Left Foot FSR signal.&lt;br /&gt;
&lt;br /&gt;
* RF_HS - Sample numbers of Heel-Strike event  from Right Foot FSR signal. &lt;br /&gt;
&lt;br /&gt;
* RF_TO - Sample numbers of Toe-Off event from Right Foot FSR signal.&lt;br /&gt;
&lt;br /&gt;
[[File:GroundTruthStructure.png|1200px]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
=== 4. mainScript.m === &lt;br /&gt;
&lt;br /&gt;
This MATLAB script is an example on how to:&lt;br /&gt;
&lt;br /&gt;
* Read the accelerometer data of a subject for an activity &amp;amp; position&lt;br /&gt;
&lt;br /&gt;
* Read the Ground Truth Heel-Strike and Toe-Off gait events for the same&lt;br /&gt;
&lt;br /&gt;
* Plot all the signals&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
----&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 160%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;How to get the datasets?&amp;#039;&amp;#039;&amp;#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
&lt;br /&gt;
----------------------------------------------------------------------------------------------------------------------------------&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 110%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;1. Link to the Release Agreement and fill Registration Form:&amp;#039;&amp;#039;&amp;#039;  [http://islab.hh.se/mediawiki/Gait_database:_Release_agreement Click_Release Agreement]&amp;lt;/div&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 110%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;2. Link to download the datasets:&amp;#039;&amp;#039;&amp;#039;  Will be sent by Email after Registration Procedure is completed &amp;lt;/div&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 160%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;References&amp;#039;&amp;#039;&amp;#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
----------------------------------------------------&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 120%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;Please remember to include an appropriate citation to acknowledge the use of the database in all documents and papers that uses the MAREA Gait Database:&amp;#039;&amp;#039;&amp;#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
1. Siddhartha Khandelwal, Nicholas Wickström, Evaluation of the performance of accelerometer-based gait event detection algorithms in different real-world scenarios using the MAREA gait database, Gait &amp;amp; Posture, Volume 51, January 2017, Pages 84-90, ISSN 0966-6362, http://dx.doi.org/10.1016/j.gaitpost.2016.09.023.&lt;br /&gt;
&lt;br /&gt;
Sciencedirect: [http://www.sciencedirect.com/science/article/pii/S0966636216305859]&lt;br /&gt;
Diva: [http://hh.diva-portal.org/smash/record.jsf?searchId=1&amp;amp;pid=diva2%3A999938&amp;amp;dswid=1416]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 120%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;You may also want to read:&amp;#039;&amp;#039;&amp;#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
2. Siddhartha Khandelwal; Nicholas Wickström, &amp;quot;Gait Event Detection in Real-World Environment for Long-Term Applications: Incorporating Domain Knowledge into Time-Frequency Analysis,&amp;quot; in IEEE Transactions on Neural Systems and Rehabilitation Engineering , vol. 24, no. 12, pp.1363-1372, 2016 &lt;br /&gt;
&lt;br /&gt;
IEEE url: [http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7423805&amp;amp;newsearch=true&amp;amp;queryText=siddhartha%20khandelwal]&lt;br /&gt;
DiVA url: [http://hh.diva-portal.org/smash/record.jsf?searchId=1&amp;amp;pid=diva2:909015]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
3. Siddhartha Khandelwal, Nicholas Wickström, &amp;quot;Novel methodology for estimating Initial Contact events from accelerometers positioned at different body locations&amp;quot;, in Gait &amp;amp; Posture, Volume 59, Pages 278–285, Jan 2018.&lt;br /&gt;
&lt;br /&gt;
DOI: http://dx.doi.org/10.1016/j.gaitpost.2017.07.030&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
4. Siddhartha Khandelwal; Nicholas Wickström, &amp;quot;Identification of Gait Events using Expert Knowledge and Continuous Wavelet Transform Analysis&amp;quot;, in BIOSIGNALS 2014, 7th International Conference on Bio-inspired Systems and Signal Processing, Angers, France, March 3-6, 2014 &lt;br /&gt;
&lt;br /&gt;
DiVA url: [http://hh.diva-portal.org/smash/record.jsf?searchId=1&amp;amp;pid=diva2:688909]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
5. Siddhartha Khandelwal, Nicholas Wickström, &amp;quot;Detecting Gait Events from Outdoor Accelerometer Data for Long-term and Continuous Monitoring Applications&amp;quot; in 13th International Symposium on 3D Analysis of Human Movement (3D-AHM 2014), 14–17 July, 2014, Lausanne, Switzerland.&lt;br /&gt;
&lt;br /&gt;
DiVA url: [http://www.diva-portal.org/smash/record.jsf?pid=diva2:735193]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 160%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;Created By&amp;#039;&amp;#039;&amp;#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
----------------------------------------------------&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
[http://islab.hh.se/mediawiki/Siddhartha_Khandelwal Siddhartha Khandelwal]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
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&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
----&lt;/div&gt;</summary>
		<author><name>Siddhartha</name></author>
	</entry>
	<entry>
		<id>https://mw.hh.se/caisr/index.php?title=Siddhartha_Khandelwal&amp;diff=3849</id>
		<title>Siddhartha Khandelwal</title>
		<link rel="alternate" type="text/html" href="https://mw.hh.se/caisr/index.php?title=Siddhartha_Khandelwal&amp;diff=3849"/>
		<updated>2018-01-15T16:33:38Z</updated>

		<summary type="html">&lt;p&gt;Siddhartha: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;{{Person&lt;br /&gt;
|Family Name=Khandelwal&lt;br /&gt;
|Given Name=Siddhartha&lt;br /&gt;
|Title=M.Sc&lt;br /&gt;
|Phone=+46-35-16-7667&lt;br /&gt;
|Position=PhD Candidate&lt;br /&gt;
|Email=siddhartha.khandelwal@hh.se&lt;br /&gt;
|Image=Sid.jpg&lt;br /&gt;
|Country=Sweden&lt;br /&gt;
|Office=E522&lt;br /&gt;
|url= &lt;br /&gt;
|Subject=Human Motion Analysis using Wearable Sensors&lt;br /&gt;
}}&lt;br /&gt;
{{AssignProjects&lt;br /&gt;
|project=HMC2&lt;br /&gt;
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{{AssignSubjectAreas&lt;br /&gt;
|SubjectArea= Signal Analysis&lt;br /&gt;
}}&lt;br /&gt;
{{AssignApplicationAreas&lt;br /&gt;
|ApplicationArea= Health Technology&lt;br /&gt;
}}&lt;br /&gt;
{{AssignExaminer&lt;br /&gt;
|Examiner=Matlab with Applications (7.5 credits)&lt;br /&gt;
}}&lt;br /&gt;
{{AssignExaminer&lt;br /&gt;
|Examiner=Robotic Manipulators (Teaching Assistant, 7.5 credits)&lt;br /&gt;
}}&lt;br /&gt;
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[[Category:Staff]]&lt;br /&gt;
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&amp;lt;!--Remove or add comments --&amp;gt;&lt;br /&gt;
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&lt;br /&gt;
== About Siddhartha ==&lt;br /&gt;
&lt;br /&gt;
After finishing his Bachelors in Electronics from VIT University, India[http://www.vit.ac.in/] and TU Dresden, Germany (Bachelor Thesis)[http://tu-dresden.de/en], he worked for almost an year in a Robotics Start-up called ThinkLabs at IIT, Bombay (India)[http://www.thinklabs.in/]. Then he received the Erasmus-Mundus scholarship &amp;quot;EMARO&amp;quot; [http://emaro.irccyn.ec-nantes.fr/] to pursue Masters in Robotics at Warsaw University of Technology, Poland [http://www.pw.edu.pl/engpw] and Ecole Centrale Nantes, France [http://www.ec-nantes.fr/version-anglaise/]. In Nov 2012, he joined CAISR at Halmstad University to pursue his PhD on Human Motion Analysis using Wearable Sensors. His current research involves quantitative and qualitative analysis of human motion using wearable sensors (especially accelerometers) with focus on gait analysis, involving:&lt;br /&gt;
&lt;br /&gt;
* Gait event detection in real-world environments for long-term and continuous monitoring applications&lt;br /&gt;
* Activity classification and characterization of gait using wearable sensors &lt;br /&gt;
* Early prediction and continuous monitoring of neuro-physiological diseases such as Parkinson, cerebral palsy, etc&lt;br /&gt;
* Analyzing the performance and technique of elite athletes with special focus on bicycling&lt;br /&gt;
* Prediction of energy expenditure using wearable sensors&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== Siddhartha elsewhere == &lt;br /&gt;
&lt;br /&gt;
[http://scholar.google.se/citations?user=9geTDAIAAAAJ&amp;amp;hl=en Google Scholar] &lt;br /&gt;
&lt;br /&gt;
[http://www.linkedin.com/in/siddhartha-khandelwal-363b7718/ LinkedIn] &lt;br /&gt;
&lt;br /&gt;
[http://www.researchgate.net/profile/Siddhartha_Khandelwal/ ResearchGate]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== MAREA Gait Database ==&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
MAREA gait database:  http://islab.hh.se/mediawiki/Gait_database &lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
{{PublicationsList}}&lt;/div&gt;</summary>
		<author><name>Siddhartha</name></author>
	</entry>
	<entry>
		<id>https://mw.hh.se/caisr/index.php?title=Siddhartha_Khandelwal&amp;diff=3453</id>
		<title>Siddhartha Khandelwal</title>
		<link rel="alternate" type="text/html" href="https://mw.hh.se/caisr/index.php?title=Siddhartha_Khandelwal&amp;diff=3453"/>
		<updated>2017-04-26T12:46:50Z</updated>

		<summary type="html">&lt;p&gt;Siddhartha: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;{{Person&lt;br /&gt;
|Family Name=Khandelwal&lt;br /&gt;
|Given Name=Siddhartha&lt;br /&gt;
|Title=M.Sc&lt;br /&gt;
|Phone=+46-35-16-7667&lt;br /&gt;
|Position=PhD Candidate&lt;br /&gt;
|Email=siddhartha.khandelwal@hh.se&lt;br /&gt;
|Image=Sid.jpg&lt;br /&gt;
|Country=Sweden&lt;br /&gt;
|Office=E522&lt;br /&gt;
|url= &lt;br /&gt;
|Subject=Human Motion Analysis using Wearable Sensors&lt;br /&gt;
}}&lt;br /&gt;
{{AssignProjects&lt;br /&gt;
|project=HMC2&lt;br /&gt;
}}&lt;br /&gt;
{{AssignSubjectAreas&lt;br /&gt;
|SubjectArea= Signal Analysis&lt;br /&gt;
}}&lt;br /&gt;
{{AssignApplicationAreas&lt;br /&gt;
|ApplicationArea= Health Technology&lt;br /&gt;
}}&lt;br /&gt;
{{AssignExaminer&lt;br /&gt;
|Examiner=Matlab with Applications (7.5 credits)&lt;br /&gt;
}}&lt;br /&gt;
{{AssignExaminer&lt;br /&gt;
|Examiner=Robotic Manipulators (Teaching Assistant, 7.5 credits)&lt;br /&gt;
}}&lt;br /&gt;
&lt;br /&gt;
[[Category:Staff]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;!--Remove or add comments --&amp;gt;&lt;br /&gt;
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&lt;br /&gt;
&lt;br /&gt;
== About Siddhartha ==&lt;br /&gt;
&lt;br /&gt;
After finishing his Bachelors in Electronics from VIT University, India[http://www.vit.ac.in/] and TU Dresden, Germany (Bachelor Thesis)[http://tu-dresden.de/en], he worked for almost an year in a Robotics Start-up called ThinkLabs at IIT, Bombay (India)[http://www.thinklabs.in/]. Then he received the Erasmus-Mundus scholarship &amp;quot;EMARO&amp;quot; [http://emaro.irccyn.ec-nantes.fr/] to pursue Masters in Robotics at Warsaw University of Technology, Poland [http://www.pw.edu.pl/engpw] and Ecole Centrale Nantes, France [http://www.ec-nantes.fr/version-anglaise/]. In Nov 2012, he joined CAISR at Halmstad University to pursue his PhD on Human Motion Analysis using Wearable Sensors. His current research involves quantitative and qualitative analysis of human motion using wearable sensors (especially accelerometers) with focus on gait analysis, involving:&lt;br /&gt;
&lt;br /&gt;
* Gait event detection in real-world environments for long-term and continuous monitoring applications&lt;br /&gt;
* Activity classification and characterization of gait using wearable sensors &lt;br /&gt;
* Early prediction and continuous monitoring of neuro-physiological diseases such as Parkinson, cerebral palsy, etc&lt;br /&gt;
* Analyzing the performance and technique of elite athletes with special focus on bicycling&lt;br /&gt;
* Prediction of energy expenditure using wearable sensors&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== Siddhartha elsewhere == &lt;br /&gt;
&lt;br /&gt;
[http://scholar.google.se/citations?user=9geTDAIAAAAJ&amp;amp;hl=en Google Scholar] &lt;br /&gt;
&lt;br /&gt;
[http://www.linkedin.com/in/siddhartha-khandelwal-363b7718/ LinkedIn] &lt;br /&gt;
&lt;br /&gt;
[http://www.researchgate.net/profile/Siddhartha_Khandelwal/ ResearchGate]&lt;br /&gt;
&lt;br /&gt;
[http://yourshot.nationalgeographic.com/profile/262109/ YourShot - National Geographic]&lt;br /&gt;
&lt;br /&gt;
Cloud based mobile platform for free-living gait analysis: [http://www.youtube.com/watch?v=H6TxxXDMMSo Youtube]&lt;br /&gt;
&lt;br /&gt;
News article on Robotics Education in India: [http://epaper.timesofindia.com/Repository/getFiles.asp?Style=OliveXLib:LowLevelEntityToPrint_TOI&amp;amp;Type=text/html&amp;amp;Locale=english-skin-custom&amp;amp;Path=TOIM/2009/12/13&amp;amp;ID=Ar00202 The Times of India] &lt;br /&gt;
&lt;br /&gt;
News article on Robotics Competition in India: [http://dnasyndication.com/showarticle.aspx?nid=DNMUM156216 DNA, India] &lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== Open Source (code and data) ==&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
Code for Gait Event Detection algorithm: http://islab.hh.se/mediawiki/Gait_events &lt;br /&gt;
&lt;br /&gt;
MAREA gait database:  http://islab.hh.se/mediawiki/Gait_database &lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
{{PublicationsList}}&lt;/div&gt;</summary>
		<author><name>Siddhartha</name></author>
	</entry>
	<entry>
		<id>https://mw.hh.se/caisr/index.php?title=Siddhartha_Khandelwal&amp;diff=3452</id>
		<title>Siddhartha Khandelwal</title>
		<link rel="alternate" type="text/html" href="https://mw.hh.se/caisr/index.php?title=Siddhartha_Khandelwal&amp;diff=3452"/>
		<updated>2017-04-26T12:46:00Z</updated>

		<summary type="html">&lt;p&gt;Siddhartha: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;{{Person&lt;br /&gt;
|Family Name=Khandelwal&lt;br /&gt;
|Given Name=Siddhartha&lt;br /&gt;
|Title=M.Sc&lt;br /&gt;
|Phone=+46-35-16-7667&lt;br /&gt;
|Position=PhD Candidate&lt;br /&gt;
|Email=siddhartha.khandelwal@hh.se&lt;br /&gt;
|Image=Sid.jpg&lt;br /&gt;
|Country=Sweden&lt;br /&gt;
|Office=E522&lt;br /&gt;
|url= &lt;br /&gt;
|Subject=Human Motion Analysis using Wearable Sensors&lt;br /&gt;
}}&lt;br /&gt;
{{AssignProjects&lt;br /&gt;
|project=HMC2&lt;br /&gt;
}}&lt;br /&gt;
{{AssignSubjectAreas&lt;br /&gt;
|SubjectArea= Signal Analysis&lt;br /&gt;
}}&lt;br /&gt;
{{AssignApplicationAreas&lt;br /&gt;
|ApplicationArea= Health Technology&lt;br /&gt;
}}&lt;br /&gt;
{{AssignExaminer&lt;br /&gt;
|Examiner=Matlab with Applications (7.5 credits)&lt;br /&gt;
}}&lt;br /&gt;
{{AssignExaminer&lt;br /&gt;
|Examiner=Robotic Manipulators (Teaching Assistant, 7.5 credits)&lt;br /&gt;
}}&lt;br /&gt;
&lt;br /&gt;
[[Category:Staff]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;!--Remove or add comments --&amp;gt;&lt;br /&gt;
{{ShowPerson}}&lt;br /&gt;
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{{InsertProjects}}&lt;br /&gt;
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&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== About Siddhartha ==&lt;br /&gt;
&lt;br /&gt;
After finishing his Bachelors in Electronics from VIT University, India[http://www.vit.ac.in/] and TU Dresden, Germany (Bachelor Thesis)[http://tu-dresden.de/en], he worked for almost an year in a Robotics Start-up called ThinkLabs at IIT, Bombay (India)[http://www.thinklabs.in/]. Then he received the Erasmus-Mundus scholarship &amp;quot;EMARO&amp;quot; [http://emaro.irccyn.ec-nantes.fr/] to pursue Masters in Robotics at Warsaw University of Technology, Poland [http://www.pw.edu.pl/engpw] and Ecole Centrale Nantes, France [http://www.ec-nantes.fr/version-anglaise/]. In Nov 2012, he joined CAISR at Halmstad University to pursue his PhD on Human Motion Analysis using Wearable Sensors. His current research involves quantitative and qualitative analysis of human motion using wearable sensors (especially accelerometers) with focus on gait analysis, involving:&lt;br /&gt;
&lt;br /&gt;
* Gait event detection in real-world environments for long-term and continuous monitoring applications&lt;br /&gt;
* Activity classification and characterization of gait using wearable sensors &lt;br /&gt;
* Early prediction and continuous monitoring of neuro-physiological diseases such as Parkinson, cerebral palsy, etc&lt;br /&gt;
* Analyzing the performance and technique of elite athletes with special focus on bicycling&lt;br /&gt;
* Prediction of energy expenditure using wearable sensors&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== Siddhartha elsewhere == &lt;br /&gt;
&lt;br /&gt;
[http://scholar.google.se/citations?user=9geTDAIAAAAJ&amp;amp;hl=en Google Scholar] &lt;br /&gt;
&lt;br /&gt;
[http://www.linkedin.com/in/siddhartha-khandelwal-363b7718/ LinkedIn] &lt;br /&gt;
&lt;br /&gt;
[http://www.researchgate.net/profile/Siddhartha_Khandelwal/ ResearchGate]&lt;br /&gt;
&lt;br /&gt;
[http://yourshot.nationalgeographic.com/profile/262109/ YourShot - National Geographic]&lt;br /&gt;
&lt;br /&gt;
Cloud based mobile platform for free-living gait analysis: [http://www.youtube.com/watch?v=H6TxxXDMMSo Youtube]&lt;br /&gt;
&lt;br /&gt;
News article on Robotics Education in India: [http://epaper.timesofindia.com/Repository/getFiles.asp?Style=OliveXLib:LowLevelEntityToPrint_TOI&amp;amp;Type=text/html&amp;amp;Locale=english-skin-custom&amp;amp;Path=TOIM/2009/12/13&amp;amp;ID=Ar00202 The Times of India] &lt;br /&gt;
&lt;br /&gt;
News article on Robotics Competition in India: [http://dnasyndication.com/showarticle.aspx?nid=DNMUM156216 DNA, India] &lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== Open Source (code and data) ==&lt;br /&gt;
&lt;br /&gt;
&amp;#039;&amp;#039;&amp;#039; Code for Gait Event Detection algorithm&amp;#039;&amp;#039;&amp;#039;: http://islab.hh.se/mediawiki/Gait_events &lt;br /&gt;
&lt;br /&gt;
&amp;#039;&amp;#039;&amp;#039;MAREA gait database&amp;#039;&amp;#039;&amp;#039;:  http://islab.hh.se/mediawiki/Gait_database &lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
{{PublicationsList}}&lt;/div&gt;</summary>
		<author><name>Siddhartha</name></author>
	</entry>
	<entry>
		<id>https://mw.hh.se/caisr/index.php?title=Siddhartha_Khandelwal&amp;diff=3451</id>
		<title>Siddhartha Khandelwal</title>
		<link rel="alternate" type="text/html" href="https://mw.hh.se/caisr/index.php?title=Siddhartha_Khandelwal&amp;diff=3451"/>
		<updated>2017-04-26T12:43:54Z</updated>

		<summary type="html">&lt;p&gt;Siddhartha: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;{{Person&lt;br /&gt;
|Family Name=Khandelwal&lt;br /&gt;
|Given Name=Siddhartha&lt;br /&gt;
|Title=M.Sc&lt;br /&gt;
|Phone=+46-35-16-7667&lt;br /&gt;
|Position=PhD Candidate&lt;br /&gt;
|Email=siddhartha.khandelwal@hh.se&lt;br /&gt;
|Image=Sid.jpg&lt;br /&gt;
|Country=Sweden&lt;br /&gt;
|Office=E522&lt;br /&gt;
|url= &lt;br /&gt;
|Subject=Human Motion Analysis using Wearable Sensors&lt;br /&gt;
}}&lt;br /&gt;
{{AssignProjects&lt;br /&gt;
|project=HMC2&lt;br /&gt;
}}&lt;br /&gt;
{{AssignSubjectAreas&lt;br /&gt;
|SubjectArea= Signal Analysis&lt;br /&gt;
}}&lt;br /&gt;
{{AssignApplicationAreas&lt;br /&gt;
|ApplicationArea= Health Technology&lt;br /&gt;
}}&lt;br /&gt;
{{AssignExaminer&lt;br /&gt;
|Examiner=Matlab with Applications (7.5 credits)&lt;br /&gt;
}}&lt;br /&gt;
{{AssignExaminer&lt;br /&gt;
|Examiner=Robotic Manipulators (Teaching Assistant, 7.5 credits)&lt;br /&gt;
}}&lt;br /&gt;
&lt;br /&gt;
[[Category:Staff]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;!--Remove or add comments --&amp;gt;&lt;br /&gt;
{{ShowPerson}}&lt;br /&gt;
{{InsertTeacher}}&lt;br /&gt;
{{InsertSubjAreas}}&lt;br /&gt;
{{InsertProjects}}&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== About Siddhartha ==&lt;br /&gt;
&lt;br /&gt;
After finishing his Bachelors in Electronics from VIT University, India[http://www.vit.ac.in/] and TU Dresden, Germany (Bachelor Thesis)[http://tu-dresden.de/en], he worked for almost an year in a Robotics Start-up called ThinkLabs at IIT, Bombay (India)[http://www.thinklabs.in/]. Then he received the Erasmus-Mundus scholarship &amp;quot;EMARO&amp;quot; [http://emaro.irccyn.ec-nantes.fr/] to pursue Masters in Robotics at Warsaw University of Technology, Poland [http://www.pw.edu.pl/engpw] and Ecole Centrale Nantes, France [http://www.ec-nantes.fr/version-anglaise/]. In Nov 2012, he joined CAISR at Halmstad University to pursue his PhD on Human Motion Analysis using Wearable Sensors. His current research involves quantitative and qualitative analysis of human motion using wearable sensors (especially accelerometers) with focus on gait analysis, involving:&lt;br /&gt;
&lt;br /&gt;
* Gait event detection in real-world environments for long-term and continuous monitoring applications&lt;br /&gt;
* Activity classification and characterization of gait using wearable sensors &lt;br /&gt;
* Early prediction and continuous monitoring of neuro-physiological diseases such as Parkinson, cerebral palsy, etc&lt;br /&gt;
* Analyzing the performance and technique of elite athletes with special focus on bicycling&lt;br /&gt;
* Prediction of energy expenditure using wearable sensors&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== Siddhartha elsewhere == &lt;br /&gt;
&lt;br /&gt;
[http://scholar.google.se/citations?user=9geTDAIAAAAJ&amp;amp;hl=en Google Scholar] &lt;br /&gt;
&lt;br /&gt;
[http://www.linkedin.com/in/siddhartha-khandelwal-363b7718/ LinkedIn] &lt;br /&gt;
&lt;br /&gt;
[http://www.researchgate.net/profile/Siddhartha_Khandelwal/ ResearchGate]&lt;br /&gt;
&lt;br /&gt;
[http://yourshot.nationalgeographic.com/profile/262109/ YourShot - National Geographic]&lt;br /&gt;
&lt;br /&gt;
Cloud based mobile platform for free-living gait analysis: [http://www.youtube.com/watch?v=H6TxxXDMMSo Youtube]&lt;br /&gt;
&lt;br /&gt;
News article on Robotics Education in India: [http://epaper.timesofindia.com/Repository/getFiles.asp?Style=OliveXLib:LowLevelEntityToPrint_TOI&amp;amp;Type=text/html&amp;amp;Locale=english-skin-custom&amp;amp;Path=TOIM/2009/12/13&amp;amp;ID=Ar00202 The Times of India] &lt;br /&gt;
&lt;br /&gt;
News article on Robotics Competition in India: [http://dnasyndication.com/showarticle.aspx?nid=DNMUM156216 DNA, India] &lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== Open Source (code and data) ==&lt;br /&gt;
&lt;br /&gt;
Link to the page to download the &amp;#039;&amp;#039;&amp;#039; code for Gait Event Detection algorithm&amp;#039;&amp;#039;&amp;#039;: http://islab.hh.se/mediawiki/Gait_events &lt;br /&gt;
&lt;br /&gt;
Link to the page to download the &amp;#039;&amp;#039;&amp;#039;MAREA gait database&amp;#039;&amp;#039;&amp;#039;:  http://islab.hh.se/mediawiki/Gait_database &lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
{{PublicationsList}}&lt;/div&gt;</summary>
		<author><name>Siddhartha</name></author>
	</entry>
	<entry>
		<id>https://mw.hh.se/caisr/index.php?title=Siddhartha_Khandelwal&amp;diff=3450</id>
		<title>Siddhartha Khandelwal</title>
		<link rel="alternate" type="text/html" href="https://mw.hh.se/caisr/index.php?title=Siddhartha_Khandelwal&amp;diff=3450"/>
		<updated>2017-04-26T12:42:28Z</updated>

		<summary type="html">&lt;p&gt;Siddhartha: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;{{Person&lt;br /&gt;
|Family Name=Khandelwal&lt;br /&gt;
|Given Name=Siddhartha&lt;br /&gt;
|Title=M.Sc&lt;br /&gt;
|Phone=+46-35-16-7667&lt;br /&gt;
|Position=PhD Candidate&lt;br /&gt;
|Email=siddhartha.khandelwal@hh.se&lt;br /&gt;
|Image=Sid.jpg&lt;br /&gt;
|Country=Sweden&lt;br /&gt;
|Office=E522&lt;br /&gt;
|url= &lt;br /&gt;
|Subject=Human Motion Analysis using Wearable Sensors&lt;br /&gt;
}}&lt;br /&gt;
{{AssignProjects&lt;br /&gt;
|project=HMC2&lt;br /&gt;
}}&lt;br /&gt;
{{AssignSubjectAreas&lt;br /&gt;
|SubjectArea= Signal Analysis&lt;br /&gt;
}}&lt;br /&gt;
{{AssignApplicationAreas&lt;br /&gt;
|ApplicationArea= Health Technology&lt;br /&gt;
}}&lt;br /&gt;
{{AssignExaminer&lt;br /&gt;
|Examiner=Matlab with Applications (7.5 credits)&lt;br /&gt;
}}&lt;br /&gt;
{{AssignExaminer&lt;br /&gt;
|Examiner=Robotic Manipulators (Teaching Assistant, 7.5 credits)&lt;br /&gt;
}}&lt;br /&gt;
&lt;br /&gt;
[[Category:Staff]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;!--Remove or add comments --&amp;gt;&lt;br /&gt;
{{ShowPerson}}&lt;br /&gt;
{{InsertTeacher}}&lt;br /&gt;
{{InsertSubjAreas}}&lt;br /&gt;
{{InsertProjects}}&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== About Siddhartha ==&lt;br /&gt;
&lt;br /&gt;
After finishing his Bachelors in Electronics from VIT University, India[http://www.vit.ac.in/] and TU Dresden, Germany (Bachelor Thesis)[http://tu-dresden.de/en], he worked for almost an year in a Robotics Start-up called ThinkLabs at IIT, Bombay (India)[http://www.thinklabs.in/]. Then he received the Erasmus-Mundus scholarship &amp;quot;EMARO&amp;quot; [http://emaro.irccyn.ec-nantes.fr/] to pursue Masters in Robotics at Warsaw University of Technology, Poland [http://www.pw.edu.pl/engpw] and Ecole Centrale Nantes, France [http://www.ec-nantes.fr/version-anglaise/]. In Nov 2012, he joined CAISR at Halmstad University to pursue his PhD on Human Motion Analysis using Wearable Sensors. His current research involves quantitative and qualitative analysis of human motion using wearable sensors (especially accelerometers) with focus on gait analysis, involving:&lt;br /&gt;
&lt;br /&gt;
* Gait event detection in real-world environments for long-term and continuous monitoring applications&lt;br /&gt;
* Activity classification and characterization of gait using wearable sensors &lt;br /&gt;
* Early prediction and continuous monitoring of neuro-physiological diseases such as Parkinson, cerebral palsy, etc&lt;br /&gt;
* Analyzing the performance and technique of elite athletes with special focus on bicycling&lt;br /&gt;
* Prediction of energy expenditure using wearable sensors&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== Siddhartha elsewhere == &lt;br /&gt;
&lt;br /&gt;
[http://scholar.google.se/citations?user=9geTDAIAAAAJ&amp;amp;hl=en Google Scholar] &lt;br /&gt;
&lt;br /&gt;
[http://www.linkedin.com/in/siddhartha-khandelwal-363b7718/ LinkedIn] &lt;br /&gt;
&lt;br /&gt;
[http://www.researchgate.net/profile/Siddhartha_Khandelwal/ ResearchGate]&lt;br /&gt;
&lt;br /&gt;
[http://yourshot.nationalgeographic.com/profile/262109/ YourShot - National Geographic]&lt;br /&gt;
&lt;br /&gt;
Cloud based mobile platform for free-living gait analysis: [https://www.youtube.com/watch?v=H6TxxXDMMSo Youtube]&lt;br /&gt;
&lt;br /&gt;
News article on Robotics Education in India: [http://epaper.timesofindia.com/Repository/getFiles.asp?Style=OliveXLib:LowLevelEntityToPrint_TOI&amp;amp;Type=text/html&amp;amp;Locale=english-skin-custom&amp;amp;Path=TOIM/2009/12/13&amp;amp;ID=Ar00202 The Times of India] &lt;br /&gt;
&lt;br /&gt;
News article on Robotics Competition in India: [http://dnasyndication.com/showarticle.aspx?nid=DNMUM156216 DNA, India] &lt;br /&gt;
&lt;br /&gt;
== Open Source (code and data) ==&lt;br /&gt;
&lt;br /&gt;
Link to the page to download the &amp;#039;&amp;#039;&amp;#039; code for Gait Event Detection algorithm&amp;#039;&amp;#039;&amp;#039;: http://islab.hh.se/mediawiki/Gait_events &lt;br /&gt;
&lt;br /&gt;
Link to the page to download the &amp;#039;&amp;#039;&amp;#039;MAREA gait database&amp;#039;&amp;#039;&amp;#039;:  http://islab.hh.se/mediawiki/Gait_database &lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
{{PublicationsList}}&lt;/div&gt;</summary>
		<author><name>Siddhartha</name></author>
	</entry>
	<entry>
		<id>https://mw.hh.se/caisr/index.php?title=Siddhartha_Khandelwal&amp;diff=3449</id>
		<title>Siddhartha Khandelwal</title>
		<link rel="alternate" type="text/html" href="https://mw.hh.se/caisr/index.php?title=Siddhartha_Khandelwal&amp;diff=3449"/>
		<updated>2017-04-26T12:40:08Z</updated>

		<summary type="html">&lt;p&gt;Siddhartha: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;{{Person&lt;br /&gt;
|Family Name=Khandelwal&lt;br /&gt;
|Given Name=Siddhartha&lt;br /&gt;
|Title=M.Sc&lt;br /&gt;
|Phone=+46-35-16-7667&lt;br /&gt;
|Position=PhD Candidate&lt;br /&gt;
|Email=siddhartha.khandelwal@hh.se&lt;br /&gt;
|Image=Sid.jpg&lt;br /&gt;
|Country=Sweden&lt;br /&gt;
|Office=E522&lt;br /&gt;
|url= &lt;br /&gt;
|Subject=Human Motion Analysis using Wearable Sensors&lt;br /&gt;
}}&lt;br /&gt;
{{AssignProjects&lt;br /&gt;
|project=HMC2&lt;br /&gt;
}}&lt;br /&gt;
{{AssignSubjectAreas&lt;br /&gt;
|SubjectArea= Signal Analysis&lt;br /&gt;
}}&lt;br /&gt;
{{AssignApplicationAreas&lt;br /&gt;
|ApplicationArea= Health Technology&lt;br /&gt;
}}&lt;br /&gt;
{{AssignExaminer&lt;br /&gt;
|Examiner=Matlab with Applications (7.5 credits)&lt;br /&gt;
}}&lt;br /&gt;
{{AssignExaminer&lt;br /&gt;
|Examiner=Robotic Manipulators (Teaching Assistant, 7.5 credits)&lt;br /&gt;
}}&lt;br /&gt;
&lt;br /&gt;
[[Category:Staff]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;!--Remove or add comments --&amp;gt;&lt;br /&gt;
{{ShowPerson}}&lt;br /&gt;
{{InsertTeacher}}&lt;br /&gt;
{{InsertSubjAreas}}&lt;br /&gt;
{{InsertProjects}}&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== About Siddhartha ==&lt;br /&gt;
&lt;br /&gt;
After finishing my Bachelors in Electronics from Vellore Institute of Technology, India[http://www.vit.ac.in/] and TU Dresden, Germany (Bachelor Thesis)[http://tu-dresden.de/en], I worked for almost an year in a Robotics Start-up called ThinkLabs at IIT, Bombay (India)[http://www.thinklabs.in/]. Then I received the Erasmus-Mundus scholarship &amp;quot;EMARO&amp;quot; [http://emaro.irccyn.ec-nantes.fr/] to pursue Masters in Robotics at WUT, Poland [http://www.pw.edu.pl/engpw] and ECN, France [http://www.ec-nantes.fr/version-anglaise/]. In 2012, I joined IS-Lab at Halmstad University to pursue my PhD on Human Motion Analysis using Wearable Sensors. My current research involves quantitative and qualitative analysis of human motion using wearable sensors (especially accelerometers) with focus on gait analysis, involving:&lt;br /&gt;
&lt;br /&gt;
* Gait event detection in real-world environments for long-term and continuous monitoring applications&lt;br /&gt;
* Activity classification and characterization of gait using wearable sensors &lt;br /&gt;
* Early prediction and continuous monitoring of neuro-physiological diseases such as Parkinson, cerebral palsy, etc&lt;br /&gt;
* Analyzing the performance and technique of elite athletes with special focus on bicycling&lt;br /&gt;
* Prediction of energy expenditure using wearable sensors&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== Siddhartha elsewhere == &lt;br /&gt;
&lt;br /&gt;
[http://scholar.google.se/citations?user=9geTDAIAAAAJ&amp;amp;hl=en Google Scholar] &lt;br /&gt;
&lt;br /&gt;
[http://www.linkedin.com/in/siddhartha-khandelwal-363b7718/ LinkedIn] &lt;br /&gt;
&lt;br /&gt;
[http://www.researchgate.net/profile/Siddhartha_Khandelwal/ ResearchGate]&lt;br /&gt;
&lt;br /&gt;
[http://yourshot.nationalgeographic.com/profile/262109/ YourShot - National Geographic]&lt;br /&gt;
&lt;br /&gt;
Cloud based mobile platform for free-living gait analysis: [https://www.youtube.com/watch?v=H6TxxXDMMSo Youtube]&lt;br /&gt;
&lt;br /&gt;
News article on Robotics Education in India: [http://epaper.timesofindia.com/Repository/getFiles.asp?Style=OliveXLib:LowLevelEntityToPrint_TOI&amp;amp;Type=text/html&amp;amp;Locale=english-skin-custom&amp;amp;Path=TOIM/2009/12/13&amp;amp;ID=Ar00202 The Times of India] &lt;br /&gt;
&lt;br /&gt;
News article on Robotics Competition in India: [http://dnasyndication.com/showarticle.aspx?nid=DNMUM156216 DNA, India] &lt;br /&gt;
&lt;br /&gt;
== Open Source (code and data) ==&lt;br /&gt;
&lt;br /&gt;
Link to the page to download the &amp;#039;&amp;#039;&amp;#039; code for Gait Event Detection algorithm&amp;#039;&amp;#039;&amp;#039;: http://islab.hh.se/mediawiki/Gait_events &lt;br /&gt;
&lt;br /&gt;
Link to the page to download the &amp;#039;&amp;#039;&amp;#039;MAREA gait database&amp;#039;&amp;#039;&amp;#039;:  http://islab.hh.se/mediawiki/Gait_database &lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
{{PublicationsList}}&lt;/div&gt;</summary>
		<author><name>Siddhartha</name></author>
	</entry>
	<entry>
		<id>https://mw.hh.se/caisr/index.php?title=Siddhartha_Khandelwal&amp;diff=3448</id>
		<title>Siddhartha Khandelwal</title>
		<link rel="alternate" type="text/html" href="https://mw.hh.se/caisr/index.php?title=Siddhartha_Khandelwal&amp;diff=3448"/>
		<updated>2017-04-26T12:37:52Z</updated>

		<summary type="html">&lt;p&gt;Siddhartha: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;{{Person&lt;br /&gt;
|Family Name=Khandelwal&lt;br /&gt;
|Given Name=Siddhartha&lt;br /&gt;
|Title=M.Sc&lt;br /&gt;
|Phone=+46-35-16-7667&lt;br /&gt;
|Position=PhD Candidate&lt;br /&gt;
|Email=siddhartha.khandelwal@hh.se&lt;br /&gt;
|Image=Sid.jpg&lt;br /&gt;
|Country=Sweden&lt;br /&gt;
|Office=E522&lt;br /&gt;
|url= &lt;br /&gt;
|Subject=Human Motion Analysis using Wearable Sensors&lt;br /&gt;
}}&lt;br /&gt;
{{AssignProjects&lt;br /&gt;
|project=HMC2&lt;br /&gt;
}}&lt;br /&gt;
{{AssignSubjectAreas&lt;br /&gt;
|SubjectArea= Signal Analysis&lt;br /&gt;
}}&lt;br /&gt;
{{AssignApplicationAreas&lt;br /&gt;
|ApplicationArea= Health Technology&lt;br /&gt;
}}&lt;br /&gt;
{{AssignExaminer&lt;br /&gt;
|Examiner=Matlab with Applications (7.5 credits)&lt;br /&gt;
}}&lt;br /&gt;
{{AssignExaminer&lt;br /&gt;
|Examiner=Robotic Manipulators (Teaching Assistant, 7.5 credits)&lt;br /&gt;
}}&lt;br /&gt;
&lt;br /&gt;
[[Category:Staff]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;!--Remove or add comments --&amp;gt;&lt;br /&gt;
{{ShowPerson}}&lt;br /&gt;
{{InsertTeacher}}&lt;br /&gt;
{{InsertSubjAreas}}&lt;br /&gt;
{{InsertProjects}}&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== About Siddhartha ==&lt;br /&gt;
&lt;br /&gt;
After finishing my Bachelors in Electronics from Vellore Institute of Technology, India[http://www.vit.ac.in/] and TU Dresden, Germany (Bachelor Thesis)[http://tu-dresden.de/en], I worked for almost an year in a Robotics Start-up called ThinkLabs at IIT, Bombay (India)[http://www.thinklabs.in/]. Then I received the Erasmus-Mundus scholarship &amp;quot;EMARO&amp;quot; [http://emaro.irccyn.ec-nantes.fr/] to pursue Masters in Robotics at WUT, Poland [http://www.pw.edu.pl/engpw] and ECN, France [http://www.ec-nantes.fr/version-anglaise/]. In 2012, I joined IS-Lab at Halmstad University to pursue my PhD on Human Motion Analysis using Wearable Sensors. My current research involves quantitative and qualitative analysis of human motion using wearable sensors (especially accelerometers) with focus on gait analysis, involving:&lt;br /&gt;
&lt;br /&gt;
* Gait event detection in real-world environments for long-term and continuous monitoring applications&lt;br /&gt;
* Activity classification and characterization of gait using wearable sensors &lt;br /&gt;
* Early prediction and continuous monitoring of neuro-physiological diseases such as Parkinson, cerebral palsy, etc&lt;br /&gt;
* Analyzing the performance and technique of elite athletes with special focus on bicycling&lt;br /&gt;
* Prediction of energy expenditure using wearable sensors&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== Siddhartha elsewhere == &lt;br /&gt;
&lt;br /&gt;
[http://scholar.google.se/citations?user=9geTDAIAAAAJ&amp;amp;hl=en Google Scholar] &lt;br /&gt;
&lt;br /&gt;
[http://www.linkedin.com/in/siddhartha-khandelwal-363b7718/ LinkedIn] &lt;br /&gt;
&lt;br /&gt;
[http://www.researchgate.net/profile/Siddhartha_Khandelwal/ ResearchGate]&lt;br /&gt;
&lt;br /&gt;
[http://yourshot.nationalgeographic.com/profile/262109/ YourShot - National Geographic]&lt;br /&gt;
&lt;br /&gt;
Cloud based mobile platform for free-living gait analysis: [https://www.youtube.com/watch?v=H6TxxXDMMSo Youtube]&lt;br /&gt;
&lt;br /&gt;
News article on Robotics Education in India: [http://epaper.timesofindia.com/Repository/getFiles.asp?Style=OliveXLib:LowLevelEntityToPrint_TOI&amp;amp;Type=text/html&amp;amp;Locale=english-skin-custom&amp;amp;Path=TOIM/2009/12/13&amp;amp;ID=Ar00202 The Times of India] &lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== Open Source (code and data) ==&lt;br /&gt;
&lt;br /&gt;
Link to the page to download the &amp;#039;&amp;#039;&amp;#039; code for Gait Event Detection algorithm&amp;#039;&amp;#039;&amp;#039;: http://islab.hh.se/mediawiki/Gait_events &lt;br /&gt;
&lt;br /&gt;
Link to the page to download the &amp;#039;&amp;#039;&amp;#039;MAREA gait database&amp;#039;&amp;#039;&amp;#039;:  http://islab.hh.se/mediawiki/Gait_database &lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
{{PublicationsList}}&lt;/div&gt;</summary>
		<author><name>Siddhartha</name></author>
	</entry>
	<entry>
		<id>https://mw.hh.se/caisr/index.php?title=Siddhartha_Khandelwal&amp;diff=3447</id>
		<title>Siddhartha Khandelwal</title>
		<link rel="alternate" type="text/html" href="https://mw.hh.se/caisr/index.php?title=Siddhartha_Khandelwal&amp;diff=3447"/>
		<updated>2017-04-26T12:37:34Z</updated>

		<summary type="html">&lt;p&gt;Siddhartha: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;{{Person&lt;br /&gt;
|Family Name=Khandelwal&lt;br /&gt;
|Given Name=Siddhartha&lt;br /&gt;
|Title=M.Sc&lt;br /&gt;
|Phone=+46-35-16-7667&lt;br /&gt;
|Position=PhD Candidate&lt;br /&gt;
|Email=siddhartha.khandelwal@hh.se&lt;br /&gt;
|Image=Sid.jpg&lt;br /&gt;
|Country=Sweden&lt;br /&gt;
|Office=E522&lt;br /&gt;
|url= [https://www.hh.se/english/aboutthewebsite/staffathalmstaduniversity.4042.html?url=-1708965309%2Fl9%2Fhhstaff%2Fen%2Fdetail.lasso%3Fdo%3Dstart%26groupmember%3DC546C6A6-517F-4C94-8DB9-0D79C9627119&amp;amp;sv.url=12.6c5a541a1541d0fe3efdcf42 Halmstad University staff page]&lt;br /&gt;
|Subject=Human Motion Analysis using Wearable Sensors&lt;br /&gt;
}}&lt;br /&gt;
{{AssignProjects&lt;br /&gt;
|project=HMC2&lt;br /&gt;
}}&lt;br /&gt;
{{AssignSubjectAreas&lt;br /&gt;
|SubjectArea= Signal Analysis&lt;br /&gt;
}}&lt;br /&gt;
{{AssignApplicationAreas&lt;br /&gt;
|ApplicationArea= Health Technology&lt;br /&gt;
}}&lt;br /&gt;
{{AssignExaminer&lt;br /&gt;
|Examiner=Matlab with Applications (7.5 credits)&lt;br /&gt;
}}&lt;br /&gt;
{{AssignExaminer&lt;br /&gt;
|Examiner=Robotic Manipulators (Teaching Assistant, 7.5 credits)&lt;br /&gt;
}}&lt;br /&gt;
&lt;br /&gt;
[[Category:Staff]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;!--Remove or add comments --&amp;gt;&lt;br /&gt;
{{ShowPerson}}&lt;br /&gt;
{{InsertTeacher}}&lt;br /&gt;
{{InsertSubjAreas}}&lt;br /&gt;
{{InsertProjects}}&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== About Siddhartha ==&lt;br /&gt;
&lt;br /&gt;
After finishing my Bachelors in Electronics from Vellore Institute of Technology, India[http://www.vit.ac.in/] and TU Dresden, Germany (Bachelor Thesis)[http://tu-dresden.de/en], I worked for almost an year in a Robotics Start-up called ThinkLabs at IIT, Bombay (India)[http://www.thinklabs.in/]. Then I received the Erasmus-Mundus scholarship &amp;quot;EMARO&amp;quot; [http://emaro.irccyn.ec-nantes.fr/] to pursue Masters in Robotics at WUT, Poland [http://www.pw.edu.pl/engpw] and ECN, France [http://www.ec-nantes.fr/version-anglaise/]. In 2012, I joined IS-Lab at Halmstad University to pursue my PhD on Human Motion Analysis using Wearable Sensors. My current research involves quantitative and qualitative analysis of human motion using wearable sensors (especially accelerometers) with focus on gait analysis, involving:&lt;br /&gt;
&lt;br /&gt;
* Gait event detection in real-world environments for long-term and continuous monitoring applications&lt;br /&gt;
* Activity classification and characterization of gait using wearable sensors &lt;br /&gt;
* Early prediction and continuous monitoring of neuro-physiological diseases such as Parkinson, cerebral palsy, etc&lt;br /&gt;
* Analyzing the performance and technique of elite athletes with special focus on bicycling&lt;br /&gt;
* Prediction of energy expenditure using wearable sensors&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== Siddhartha elsewhere == &lt;br /&gt;
&lt;br /&gt;
[http://scholar.google.se/citations?user=9geTDAIAAAAJ&amp;amp;hl=en Google Scholar] &lt;br /&gt;
&lt;br /&gt;
[http://www.linkedin.com/in/siddhartha-khandelwal-363b7718/ LinkedIn] &lt;br /&gt;
&lt;br /&gt;
[http://www.researchgate.net/profile/Siddhartha_Khandelwal/ ResearchGate]&lt;br /&gt;
&lt;br /&gt;
[http://yourshot.nationalgeographic.com/profile/262109/ YourShot - National Geographic]&lt;br /&gt;
&lt;br /&gt;
Cloud based mobile platform for free-living gait analysis: [https://www.youtube.com/watch?v=H6TxxXDMMSo Youtube]&lt;br /&gt;
&lt;br /&gt;
News article on Robotics Education in India: [http://epaper.timesofindia.com/Repository/getFiles.asp?Style=OliveXLib:LowLevelEntityToPrint_TOI&amp;amp;Type=text/html&amp;amp;Locale=english-skin-custom&amp;amp;Path=TOIM/2009/12/13&amp;amp;ID=Ar00202 The Times of India] &lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== Open Source (code and data) ==&lt;br /&gt;
&lt;br /&gt;
Link to the page to download the &amp;#039;&amp;#039;&amp;#039; code for Gait Event Detection algorithm&amp;#039;&amp;#039;&amp;#039;: http://islab.hh.se/mediawiki/Gait_events &lt;br /&gt;
&lt;br /&gt;
Link to the page to download the &amp;#039;&amp;#039;&amp;#039;MAREA gait database&amp;#039;&amp;#039;&amp;#039;:  http://islab.hh.se/mediawiki/Gait_database &lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
{{PublicationsList}}&lt;/div&gt;</summary>
		<author><name>Siddhartha</name></author>
	</entry>
	<entry>
		<id>https://mw.hh.se/caisr/index.php?title=Siddhartha_Khandelwal&amp;diff=3446</id>
		<title>Siddhartha Khandelwal</title>
		<link rel="alternate" type="text/html" href="https://mw.hh.se/caisr/index.php?title=Siddhartha_Khandelwal&amp;diff=3446"/>
		<updated>2017-04-26T12:36:11Z</updated>

		<summary type="html">&lt;p&gt;Siddhartha: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;{{Person&lt;br /&gt;
|Family Name=Khandelwal&lt;br /&gt;
|Given Name=Siddhartha&lt;br /&gt;
|Title=M.Sc&lt;br /&gt;
|Phone=+46-35-16-7667&lt;br /&gt;
|Position=PhD Candidate&lt;br /&gt;
|Email=siddhartha.khandelwal@hh.se&lt;br /&gt;
|Image=Sid.jpg&lt;br /&gt;
|Country=Sweden&lt;br /&gt;
|Office=E522&lt;br /&gt;
|url=https://www.hh.se/english/aboutthewebsite/staffathalmstaduniversity.4042.html?url=-1708965309%2Fl9%2Fhhstaff%2Fen%2Fdetail.lasso%3Fdo%3Dstart%26groupmember%3DC546C6A6-517F-4C94-8DB9-0D79C9627119&amp;amp;sv.url=12.6c5a541a1541d0fe3efdcf42&lt;br /&gt;
|Subject=Human Motion Analysis using Wearable Sensors&lt;br /&gt;
}}&lt;br /&gt;
{{AssignProjects&lt;br /&gt;
|project=HMC2&lt;br /&gt;
}}&lt;br /&gt;
{{AssignSubjectAreas&lt;br /&gt;
|SubjectArea= Signal Analysis&lt;br /&gt;
}}&lt;br /&gt;
{{AssignApplicationAreas&lt;br /&gt;
|ApplicationArea= Health Technology&lt;br /&gt;
}}&lt;br /&gt;
{{AssignExaminer&lt;br /&gt;
|Examiner=Matlab with Applications (7.5 credits)&lt;br /&gt;
}}&lt;br /&gt;
{{AssignExaminer&lt;br /&gt;
|Examiner=Robotic Manipulators (Teaching Assistant, 7.5 credits)&lt;br /&gt;
}}&lt;br /&gt;
&lt;br /&gt;
[[Category:Staff]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;!--Remove or add comments --&amp;gt;&lt;br /&gt;
{{ShowPerson}}&lt;br /&gt;
{{InsertTeacher}}&lt;br /&gt;
{{InsertSubjAreas}}&lt;br /&gt;
{{InsertProjects}}&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== About Siddhartha ==&lt;br /&gt;
&lt;br /&gt;
After finishing my Bachelors in Electronics from Vellore Institute of Technology, India[http://www.vit.ac.in/] and TU Dresden, Germany (Bachelor Thesis)[http://tu-dresden.de/en], I worked for almost an year in a Robotics Start-up called ThinkLabs at IIT, Bombay (India)[http://www.thinklabs.in/]. Then I received the Erasmus-Mundus scholarship &amp;quot;EMARO&amp;quot; [http://emaro.irccyn.ec-nantes.fr/] to pursue Masters in Robotics at WUT, Poland [http://www.pw.edu.pl/engpw] and ECN, France [http://www.ec-nantes.fr/version-anglaise/]. In 2012, I joined IS-Lab at Halmstad University to pursue my PhD on Human Motion Analysis using Wearable Sensors. My current research involves quantitative and qualitative analysis of human motion using wearable sensors (especially accelerometers) with focus on gait analysis, involving:&lt;br /&gt;
&lt;br /&gt;
* Gait event detection in real-world environments for long-term and continuous monitoring applications&lt;br /&gt;
* Activity classification and characterization of gait using wearable sensors &lt;br /&gt;
* Early prediction and continuous monitoring of neuro-physiological diseases such as Parkinson, cerebral palsy, etc&lt;br /&gt;
* Analyzing the performance and technique of elite athletes with special focus on bicycling&lt;br /&gt;
* Prediction of energy expenditure using wearable sensors&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== Siddhartha elsewhere == &lt;br /&gt;
&lt;br /&gt;
[http://scholar.google.se/citations?user=9geTDAIAAAAJ&amp;amp;hl=en Google Scholar] &lt;br /&gt;
&lt;br /&gt;
[http://www.linkedin.com/in/siddhartha-khandelwal-363b7718/ LinkedIn] &lt;br /&gt;
&lt;br /&gt;
[http://www.researchgate.net/profile/Siddhartha_Khandelwal/ ResearchGate]&lt;br /&gt;
&lt;br /&gt;
[http://yourshot.nationalgeographic.com/profile/262109/ YourShot - National Geographic]&lt;br /&gt;
&lt;br /&gt;
Cloud based mobile platform for free-living gait analysis: [https://www.youtube.com/watch?v=H6TxxXDMMSo Youtube]&lt;br /&gt;
&lt;br /&gt;
News article on Robotics Education in India: [http://epaper.timesofindia.com/Repository/getFiles.asp?Style=OliveXLib:LowLevelEntityToPrint_TOI&amp;amp;Type=text/html&amp;amp;Locale=english-skin-custom&amp;amp;Path=TOIM/2009/12/13&amp;amp;ID=Ar00202 The Times of India] &lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== Open Source (code and data) ==&lt;br /&gt;
&lt;br /&gt;
Link to the page to download the &amp;#039;&amp;#039;&amp;#039; code for Gait Event Detection algorithm&amp;#039;&amp;#039;&amp;#039;: http://islab.hh.se/mediawiki/Gait_events &lt;br /&gt;
&lt;br /&gt;
Link to the page to download the &amp;#039;&amp;#039;&amp;#039;MAREA gait database&amp;#039;&amp;#039;&amp;#039;:  http://islab.hh.se/mediawiki/Gait_database &lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
{{PublicationsList}}&lt;/div&gt;</summary>
		<author><name>Siddhartha</name></author>
	</entry>
	<entry>
		<id>https://mw.hh.se/caisr/index.php?title=Siddhartha_Khandelwal&amp;diff=3445</id>
		<title>Siddhartha Khandelwal</title>
		<link rel="alternate" type="text/html" href="https://mw.hh.se/caisr/index.php?title=Siddhartha_Khandelwal&amp;diff=3445"/>
		<updated>2017-04-26T12:35:05Z</updated>

		<summary type="html">&lt;p&gt;Siddhartha: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;{{Person&lt;br /&gt;
|Family Name=Khandelwal&lt;br /&gt;
|Given Name=Siddhartha&lt;br /&gt;
|Title=M.Sc&lt;br /&gt;
|Phone=+46-35-16-7667&lt;br /&gt;
|Position=PhD Candidate&lt;br /&gt;
|Email=siddhartha.khandelwal@hh.se&lt;br /&gt;
|Image=Sid.jpg&lt;br /&gt;
|Country=Sweden&lt;br /&gt;
|Office=E522&lt;br /&gt;
|url=https://www.linkedin.com/in/siddhartha-khandelwal-363b7718?trk=nav_responsive_tab_profile&lt;br /&gt;
|Subject=Human Motion Analysis using Wearable Sensors&lt;br /&gt;
}}&lt;br /&gt;
{{AssignProjects&lt;br /&gt;
|project=HMC2&lt;br /&gt;
}}&lt;br /&gt;
{{AssignSubjectAreas&lt;br /&gt;
|SubjectArea= Signal Analysis&lt;br /&gt;
}}&lt;br /&gt;
{{AssignApplicationAreas&lt;br /&gt;
|ApplicationArea= Health Technology&lt;br /&gt;
}}&lt;br /&gt;
{{AssignExaminer&lt;br /&gt;
|Examiner=Matlab with Applications (7.5 credits)&lt;br /&gt;
}}&lt;br /&gt;
{{AssignExaminer&lt;br /&gt;
|Examiner=Robotic Manipulators (Teaching Assistant, 7.5 credits)&lt;br /&gt;
}}&lt;br /&gt;
&lt;br /&gt;
[[Category:Staff]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;!--Remove or add comments --&amp;gt;&lt;br /&gt;
{{ShowPerson}}&lt;br /&gt;
{{InsertTeacher}}&lt;br /&gt;
{{InsertSubjAreas}}&lt;br /&gt;
{{InsertProjects}}&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== About Siddhartha ==&lt;br /&gt;
&lt;br /&gt;
After finishing my Bachelors in Electronics from Vellore Institute of Technology, India[http://www.vit.ac.in/] and TU Dresden, Germany (Bachelor Thesis)[http://tu-dresden.de/en], I worked for almost an year in a Robotics Start-up called ThinkLabs at IIT, Bombay (India)[http://www.thinklabs.in/]. Then I received the Erasmus-Mundus scholarship &amp;quot;EMARO&amp;quot; [http://emaro.irccyn.ec-nantes.fr/] to pursue Masters in Robotics at WUT, Poland [http://www.pw.edu.pl/engpw] and ECN, France [http://www.ec-nantes.fr/version-anglaise/]. In 2012, I joined IS-Lab at Halmstad University to pursue my PhD on Human Motion Analysis using Wearable Sensors. My current research involves quantitative and qualitative analysis of human motion using wearable sensors (especially accelerometers) with focus on gait analysis, involving:&lt;br /&gt;
&lt;br /&gt;
* Gait event detection in real-world environments for long-term and continuous monitoring applications&lt;br /&gt;
* Activity classification and characterization of gait using wearable sensors &lt;br /&gt;
* Early prediction and continuous monitoring of neuro-physiological diseases such as Parkinson, cerebral palsy, etc&lt;br /&gt;
* Analyzing the performance and technique of elite athletes with special focus on bicycling&lt;br /&gt;
* Prediction of energy expenditure using wearable sensors&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== Siddhartha elsewhere == &lt;br /&gt;
&lt;br /&gt;
[http://scholar.google.se/citations?user=9geTDAIAAAAJ&amp;amp;hl=en Google Scholar] &lt;br /&gt;
&lt;br /&gt;
[http://www.linkedin.com/in/siddhartha-khandelwal-363b7718/ LinkedIn] &lt;br /&gt;
&lt;br /&gt;
[http://www.researchgate.net/profile/Siddhartha_Khandelwal/ ResearchGate]&lt;br /&gt;
&lt;br /&gt;
[http://yourshot.nationalgeographic.com/profile/262109/ YourShot - National Geographic]&lt;br /&gt;
&lt;br /&gt;
Cloud based mobile platform for free-living gait analysis: [https://www.youtube.com/watch?v=H6TxxXDMMSo Youtube]&lt;br /&gt;
&lt;br /&gt;
News article on Robotics Education in India: [http://epaper.timesofindia.com/Repository/getFiles.asp?Style=OliveXLib:LowLevelEntityToPrint_TOI&amp;amp;Type=text/html&amp;amp;Locale=english-skin-custom&amp;amp;Path=TOIM/2009/12/13&amp;amp;ID=Ar00202 The Times of India] &lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== Open Source (code and data) ==&lt;br /&gt;
&lt;br /&gt;
Link to the page to download the &amp;#039;&amp;#039;&amp;#039; code for Gait Event Detection algorithm&amp;#039;&amp;#039;&amp;#039;: http://islab.hh.se/mediawiki/Gait_events &lt;br /&gt;
&lt;br /&gt;
Link to the page to download the &amp;#039;&amp;#039;&amp;#039;MAREA gait database&amp;#039;&amp;#039;&amp;#039;:  http://islab.hh.se/mediawiki/Gait_database &lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
{{PublicationsList}}&lt;/div&gt;</summary>
		<author><name>Siddhartha</name></author>
	</entry>
	<entry>
		<id>https://mw.hh.se/caisr/index.php?title=Siddhartha_Khandelwal&amp;diff=3444</id>
		<title>Siddhartha Khandelwal</title>
		<link rel="alternate" type="text/html" href="https://mw.hh.se/caisr/index.php?title=Siddhartha_Khandelwal&amp;diff=3444"/>
		<updated>2017-04-26T12:18:51Z</updated>

		<summary type="html">&lt;p&gt;Siddhartha: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;{{Person&lt;br /&gt;
|Family Name=Khandelwal&lt;br /&gt;
|Given Name=Siddhartha&lt;br /&gt;
|Title=M.Sc&lt;br /&gt;
|Phone=+46-35-16-7667&lt;br /&gt;
|Position=PhD Candidate&lt;br /&gt;
|Email=siddhartha.khandelwal@hh.se&lt;br /&gt;
|Image=Sid.jpg&lt;br /&gt;
|Country=Sweden&lt;br /&gt;
|Office=E522&lt;br /&gt;
|url=https://www.linkedin.com/in/siddhartha-khandelwal-363b7718?trk=nav_responsive_tab_profile&lt;br /&gt;
|Subject=Human Motion Analysis using Wearable Sensors&lt;br /&gt;
}}&lt;br /&gt;
{{AssignProjects&lt;br /&gt;
|project=HMC2&lt;br /&gt;
}}&lt;br /&gt;
{{AssignSubjectAreas&lt;br /&gt;
|SubjectArea= Signal Analysis&lt;br /&gt;
}}&lt;br /&gt;
{{AssignApplicationAreas&lt;br /&gt;
|ApplicationArea= Health Technology&lt;br /&gt;
}}&lt;br /&gt;
{{AssignExaminer&lt;br /&gt;
|Examiner=Matlab with Applications (7.5 credits)&lt;br /&gt;
}}&lt;br /&gt;
{{AssignExaminer&lt;br /&gt;
|Examiner=Robotic Manipulators (Teaching Assistant, 7.5 credits)&lt;br /&gt;
}}&lt;br /&gt;
&lt;br /&gt;
[[Category:Staff]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;!--Remove or add comments --&amp;gt;&lt;br /&gt;
{{ShowPerson}}&lt;br /&gt;
{{InsertTeacher}}&lt;br /&gt;
{{InsertSubjAreas}}&lt;br /&gt;
{{InsertProjects}}&lt;br /&gt;
&lt;br /&gt;
&amp;#039;&amp;#039;&amp;#039;Siddhartha elsewhere:&amp;#039;&amp;#039;&amp;#039; [https://scholar.google.se/citations?user=9geTDAIAAAAJ&amp;amp;hl=en Google Scholar], [https://www.linkedin.com/in/siddhartha-khandelwal-363b7718/ LinkedIn], [https://www.researchgate.net/profile/Siddhartha_Khandelwal/ ResearchGate]&lt;br /&gt;
&lt;br /&gt;
== Open Access (code, data, etc.) ==&lt;br /&gt;
&lt;br /&gt;
Link to the page to download the &amp;#039;&amp;#039;&amp;#039; code for Gait Event Detection algorithm&amp;#039;&amp;#039;&amp;#039;: http://islab.hh.se/mediawiki/Gait_events &lt;br /&gt;
&lt;br /&gt;
Link to the page to download the &amp;#039;&amp;#039;&amp;#039;MAREA gait database&amp;#039;&amp;#039;&amp;#039;:  http://islab.hh.se/mediawiki/Gait_database &lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== About Siddhartha ==&lt;br /&gt;
&lt;br /&gt;
After finishing my Bachelors in Electronics from Vellore Institute of Technology, India[http://www.vit.ac.in/] and TU Dresden, Germany (Bachelor Thesis)[http://tu-dresden.de/en], I worked for almost an year in a Robotics Start-up called ThinkLabs at IIT, Bombay (India)[http://www.thinklabs.in/]. Then I received the Erasmus-Mundus scholarship &amp;quot;EMARO&amp;quot; [http://emaro.irccyn.ec-nantes.fr/] to pursue Masters in Robotics at WUT, Poland [http://www.pw.edu.pl/engpw] and ECN, France [http://www.ec-nantes.fr/version-anglaise/]. In 2012, I joined IS-Lab at Halmstad University to pursue my PhD on Human Motion Analysis using Wearable Sensors. My current research involves quantitative and qualitative analysis of human motion using wearable sensors (especially accelerometers) with focus on gait analysis, involving:&lt;br /&gt;
&lt;br /&gt;
* Gait event detection in real-world environments for long-term and continuous monitoring applications&lt;br /&gt;
* Activity classification and characterization of gait using wearable sensors &lt;br /&gt;
* Early prediction and continuous monitoring of neuro-physiological diseases such as Parkinson, cerebral palsy, etc&lt;br /&gt;
* Analyzing the performance and technique of elite athletes with special focus on bicycling&lt;br /&gt;
* Prediction of energy expenditure using wearable sensors&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
{{PublicationsList}}&lt;/div&gt;</summary>
		<author><name>Siddhartha</name></author>
	</entry>
	<entry>
		<id>https://mw.hh.se/caisr/index.php?title=File:MAREA_dataset.zip&amp;diff=3394</id>
		<title>File:MAREA dataset.zip</title>
		<link rel="alternate" type="text/html" href="https://mw.hh.se/caisr/index.php?title=File:MAREA_dataset.zip&amp;diff=3394"/>
		<updated>2016-12-14T14:03:49Z</updated>

		<summary type="html">&lt;p&gt;Siddhartha: Siddhartha uploaded a new version of &amp;amp;quot;File:MAREA dataset.zip&amp;amp;quot;&lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;&lt;/div&gt;</summary>
		<author><name>Siddhartha</name></author>
	</entry>
	<entry>
		<id>https://mw.hh.se/caisr/index.php?title=Gait_database&amp;diff=3393</id>
		<title>Gait database</title>
		<link rel="alternate" type="text/html" href="https://mw.hh.se/caisr/index.php?title=Gait_database&amp;diff=3393"/>
		<updated>2016-12-14T13:34:16Z</updated>

		<summary type="html">&lt;p&gt;Siddhartha: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== MAREA (&amp;#039;&amp;#039;&amp;#039;M&amp;#039;&amp;#039;&amp;#039;ovement &amp;#039;&amp;#039;&amp;#039;A&amp;#039;&amp;#039;&amp;#039;nalysis in &amp;#039;&amp;#039;&amp;#039;R&amp;#039;&amp;#039;&amp;#039;eal-world &amp;#039;&amp;#039;&amp;#039;E&amp;#039;&amp;#039;&amp;#039;nvironments using &amp;#039;&amp;#039;&amp;#039;A&amp;#039;&amp;#039;&amp;#039;ccelerometers) Gait Database ==&lt;br /&gt;
&lt;br /&gt;
[[File:accPlacement.jpg|right|thumb|200px|caption|&amp;quot;Figure 1: Position and orientation of each accelerometer at the beginning of each experiment. The accelerometer at the right ankle is attached arbitrarily without any pre-defined orientation. The FSRs are instrumented into the shoe soles to collect the ground truth. The data from all sensors is sampled at a frequency of 128 Hz.&amp;quot;]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
The &amp;#039;&amp;#039;&amp;#039;MAREA&amp;#039;&amp;#039;&amp;#039; gait database comprises of gait activities in different real-world environments as shown in the table below. 20 healthy adults (12 males and 8 females, average age: 33.4 +- 7 years, average mass: 73.2 +- 10.9 kg, average height: 172.6 +- 9.5 cm) participated in the study that was approved by the Ethical Review Board of Lund, Sweden. Each subject had a 3-axes Shimmer3 (Shimmer Research, Dublin, Ireland) accelerometer (+- 8g) attached to their waist, left wrist and left and right ankles using elastic bands and velcro straps. Figure 1 shows the position and orientation of each accelerometer at the beginning of each experiment. On the waist, the accelerometer X and Y axes were pointing to the lateral and downward direction, respectively. On the wrist and left ankle, the Z axis was pointing in the lateral direction while the Y axis was pointing downward and was aligned with the limb longitudinal axis. In order to simulate a lesser controlled scenario, the accelerometer on right ankle was positioned such that the Y axis was pointing downward but the Z axis was marginally disturbed such that it was not exactly perpendicular to the sagittal plane. The subjects were provided shoes that were instrumented with piezo-electric force sensitive resistors (FSRs), fixed at the extreme ends of the sole in order to provide the ground truth values for HS and TO. An external expansion board was used to synchronously collect the data from the FSRs on each foot and the respective ankle accelerometer, at a sampling frequency of 128Hz, and stored locally on the Shimmer3 microSD card. Due to the lack of a centralized data logger, the waist accelerometer was not in perfect synchronization with the ankle accelerometer.&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
{| class=&amp;quot;wikitable&amp;quot;&lt;br /&gt;
! style=&amp;quot;text-align: center;&amp;quot; | Subjects&lt;br /&gt;
! style=&amp;quot;text-align: center;&amp;quot; | Environment&lt;br /&gt;
! style=&amp;quot;text-align: center;&amp;quot; | Activity&lt;br /&gt;
! style=&amp;quot;text-align: center;&amp;quot; | Speed&lt;br /&gt;
! style=&amp;quot;text-align: center;&amp;quot; | Duration&lt;br /&gt;
! style=&amp;quot;text-align: center;&amp;quot; | Short Description&lt;br /&gt;
|-&lt;br /&gt;
| rowspan=&amp;quot;4&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | 11&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | Treadmill (flat)&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | Walk &amp;amp; run&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | 4km/hr - 8km/hr; increasing in steps&lt;br /&gt;
 of 0.4km/hr every minute&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | 10 min&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | Start walking and switch to &lt;br /&gt;
running at self-selected speed&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | Treadmill (slope)&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | Walk&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | Self-selected&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | 12 min&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | Treadmill is set to (5, 0, 10, 0, 15, 0) degree&lt;br /&gt;
inclinations with 2 mins at each angle&lt;br /&gt;
|-&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Indoor flat space&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Walk &amp;amp; run&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Self-selected&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | 6 min&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Start walking and switch &lt;br /&gt;
to running after 3 mins&lt;br /&gt;
|-&lt;br /&gt;
 |&lt;br /&gt;
|-&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | 9&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Outdoor street&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Walk &amp;amp; run&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Self-selected&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | 6 min&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Start walking and switch &lt;br /&gt;
to running after 3 mins&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
|}&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
==&amp;#039;&amp;#039;&amp;#039;Explanation of the database files&amp;#039;&amp;#039;&amp;#039; (download link below)==&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
=== 1. Activity Timings === &lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
There are two matrices provided, namely, &amp;#039;&amp;#039;&amp;#039;Indoor Experiment timings&amp;#039;&amp;#039;&amp;#039; and  &amp;#039;&amp;#039;&amp;#039;Outdoor Experiment timings&amp;#039;&amp;#039;&amp;#039; which contain the timing information of the activities and are included in a folder called Activity Timings.zip: &lt;br /&gt;
&lt;br /&gt;
[[File:ActivityTimingsGraph.png|right|thumb|500px|caption|&amp;quot;Example of how to use the sample numbers given in Indoor Experiment Timings matrix to extract desired activity data. This figure shows the entire Indoor data collected from accelerometer positioned at Right Ankle for Subject 5. &amp;quot;]]&lt;br /&gt;
&lt;br /&gt;
* &amp;#039;&amp;#039;&amp;#039;Indoor Experiment Timings&amp;#039;&amp;#039;&amp;#039;: This 11(rows) x 8(columns) matrix consists of &amp;#039;&amp;#039;&amp;#039;sample numbers&amp;#039;&amp;#039;&amp;#039; corresponding to the start and end of an activity, for a given subject, for the &amp;#039;&amp;#039;&amp;#039;Indoor Experiments&amp;#039;&amp;#039;&amp;#039;, i.e. &lt;br /&gt;
**Treadmill (flat)&lt;br /&gt;
**Treadmill (slope) &lt;br /&gt;
**Indoor flat space. &lt;br /&gt;
The figure below explains how to extract the sample nos. corresponding to an activity for a given subject, from the Indoor Experiment timings matrix.  &lt;br /&gt;
&lt;br /&gt;
[[File:Indoor Table.png|700px]]&lt;br /&gt;
&lt;br /&gt;
* &amp;#039;&amp;#039;&amp;#039;Outdoor Experiment Timings&amp;#039;&amp;#039;&amp;#039;: This 9(rows) x 3(columns) matrix consists of &amp;#039;&amp;#039;&amp;#039;sample numbers&amp;#039;&amp;#039;&amp;#039; corresponding to the start and end of an activity, for a given subject, for the &amp;#039;&amp;#039;&amp;#039;Outdoor Street Experiments&amp;#039;&amp;#039;&amp;#039;. The figure below explains how to extract the sample nos. corresponding to an activity for a given subject, from the Outdoor Experiment timings matrix.&lt;br /&gt;
&lt;br /&gt;
[[File:Outdoor Table.png|800px]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
=== 2. Subject Data === &lt;br /&gt;
&lt;br /&gt;
[[File:AccXYZ.png|right|thumb|400px|caption|&amp;quot;Figure 2: Example of the accelerometer data from the X, Y and Z axis for Sub5_RF&amp;quot;]]&lt;br /&gt;
&lt;br /&gt;
The Subject data files are provided in two formats, namely, .mat format (&amp;#039;&amp;#039;&amp;#039;Subject Data_mat format.zip&amp;#039;&amp;#039;&amp;#039;) and .txt format &amp;#039;&amp;#039;&amp;#039;Subject Data_txt format).zip&amp;#039;&amp;#039;&amp;#039;. The naming convention of the files is: Sub&amp;lt;number&amp;gt;_&amp;lt;position&amp;gt;, where:&lt;br /&gt;
*&amp;lt;number&amp;gt;: stands for Subject number and ranges from 1 to 20. &lt;br /&gt;
*&amp;lt;position&amp;gt;: stands for the position of the accelerometer on the body as shown in Figure 1. The positions are:&lt;br /&gt;
**LF - Left Ankle&lt;br /&gt;
**RF - Right Ankle&lt;br /&gt;
**Wrist&lt;br /&gt;
**Waist&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
For each Subject file, eg. Sub5_RF, the accelerometer data from the 3 axis accelerometer is stored in 3 columns (separated using comma in the .txt files), each named as: &lt;br /&gt;
*accX - data from X - axis&lt;br /&gt;
*accY - data from Y - axis&lt;br /&gt;
*accZ - data from Z - axis&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
===3. Ground Truth===&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
The Ground Truth is provided as a 11x7 structure that gives the timing of the Heel-Strike and Toe-Off events extracted from the FSR signals. This timing information is provided in terms of sample numbers and is relative to each activity. The structure consists of 7 fields that have &amp;#039;&amp;#039;&amp;#039;already been segregated&amp;#039;&amp;#039;&amp;#039; using the Indoor Experiment Timings and Outdoor Experiment Timings explained above: &lt;br /&gt;
*treadWalk       - treadmill (flat) walk &lt;br /&gt;
&lt;br /&gt;
*treadIncline    - treadmill (slope) walk&lt;br /&gt;
&lt;br /&gt;
*treadWalknRun   - treadmill (flat) walk &amp;amp; run&lt;br /&gt;
&lt;br /&gt;
*indoorWalk      - indoor (flat space) walk&lt;br /&gt;
&lt;br /&gt;
*indoorWalknRun  - indoor (flat space) walk &amp;amp; run&lt;br /&gt;
&lt;br /&gt;
*outdoorWalk     - outdoor street walk&lt;br /&gt;
&lt;br /&gt;
*outdoorWalknRun - outdoor street walk &amp;amp; run &lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
Each row of the structure represents a Subject. For Indoor activities, Row 1 -&amp;gt; Subject1, ..., Row 11 -&amp;gt; Subject 11 and so on. For outdoor activities, Row 1 -&amp;gt; Subject 12,..., Row 9 -&amp;gt; Subject 20. As an example: GroundTruth(1).treadWalk provides the ground truth data for Subject 1 for treadmill (flat) walk. This is a structure with fields: &lt;br /&gt;
* SubIdx - Subject number. Ranges from Sub1 - Sub20&lt;br /&gt;
&lt;br /&gt;
* LF_HS - Sample numbers of Heel-Strike event from Left Foot FSR signal.&lt;br /&gt;
&lt;br /&gt;
* LF_TO - Sample numbers of Toe-Off event from Left Foot FSR signal.&lt;br /&gt;
&lt;br /&gt;
* RF_HS - Sample numbers of Heel-Strike event  from Right Foot FSR signal. &lt;br /&gt;
&lt;br /&gt;
* RF_TO - Sample numbers of Toe-Off event from Right Foot FSR signal.&lt;br /&gt;
&lt;br /&gt;
[[File:GroundTruthStructure.png|1200px]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
=== 4. mainScript.m === &lt;br /&gt;
&lt;br /&gt;
This MATLAB script is an example on how to:&lt;br /&gt;
&lt;br /&gt;
* Read the accelerometer data of a subject for an activity &amp;amp; position&lt;br /&gt;
&lt;br /&gt;
* Read the Ground Truth Heel-Strike and Toe-Off gait events for the same&lt;br /&gt;
&lt;br /&gt;
* Plot all the signals&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
----&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 160%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;How to get the datasets?&amp;#039;&amp;#039;&amp;#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
&lt;br /&gt;
----------------------------------------------------------------------------------------------------------------------------------&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 110%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;1. Link to the Release Agreement and fill Registration Form:&amp;#039;&amp;#039;&amp;#039;  [http://islab.hh.se/mediawiki/Gait_database:_Release_agreement Click_Release Agreement]&amp;lt;/div&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 110%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;2. Link to download the datasets:&amp;#039;&amp;#039;&amp;#039;  Will be sent by Email after Registration Procedure is completed &amp;lt;/div&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 110%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;3. Link to download the code for Gait Event Detection algorithm:&amp;#039;&amp;#039;&amp;#039;  [http://islab.hh.se/mediawiki/Gait_events Gait Events]&amp;lt;/div&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 160%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;References&amp;#039;&amp;#039;&amp;#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
----------------------------------------------------&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 120%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;Please remember to include an appropriate citation to acknowledge the use of the database in all documents and papers that uses the MAREA Gait Database:&amp;#039;&amp;#039;&amp;#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
1. Siddhartha Khandelwal, Nicholas Wickström, Evaluation of the performance of accelerometer-based gait event detection algorithms in different real-world scenarios using the MAREA gait database, Gait &amp;amp; Posture, Volume 51, January 2017, Pages 84-90, ISSN 0966-6362, http://dx.doi.org/10.1016/j.gaitpost.2016.09.023.&lt;br /&gt;
&lt;br /&gt;
Sciencedirect: [http://www.sciencedirect.com/science/article/pii/S0966636216305859]&lt;br /&gt;
Diva: [http://hh.diva-portal.org/smash/record.jsf?searchId=1&amp;amp;pid=diva2%3A999938&amp;amp;dswid=1416]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
2. Siddhartha Khandelwal; Nicholas Wickström, &amp;quot;Gait Event Detection in Real-World Environment for Long-Term Applications: Incorporating Domain Knowledge into Time-Frequency Analysis,&amp;quot; in IEEE Transactions on Neural Systems and Rehabilitation Engineering , vol. 24, no. 12, pp.1363-1372, 2016 &lt;br /&gt;
&lt;br /&gt;
IEEE url: [http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7423805&amp;amp;newsearch=true&amp;amp;queryText=siddhartha%20khandelwal]&lt;br /&gt;
DiVA url: [http://hh.diva-portal.org/smash/record.jsf?searchId=1&amp;amp;pid=diva2:909015]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 120%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;You may also want to read:&amp;#039;&amp;#039;&amp;#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
3. Siddhartha Khandelwal; Nicholas Wickström, &amp;quot;Identification of Gait Events using Expert Knowledge and Continuous Wavelet Transform Analysis&amp;quot;, in BIOSIGNALS 2014, 7th International Conference on Bio-inspired Systems and Signal Processing, Angers, France, March 3-6, 2014 &lt;br /&gt;
&lt;br /&gt;
DiVA url: [http://hh.diva-portal.org/smash/record.jsf?searchId=1&amp;amp;pid=diva2:688909]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
4. Siddhartha Khandelwal, Nicholas Wickström, &amp;quot;Detecting Gait Events from Outdoor Accelerometer Data for Long-term and Continuous Monitoring Applications&amp;quot; in 13th International Symposium on 3D Analysis of Human Movement (3D-AHM 2014), 14–17 July, 2014, Lausanne, Switzerland.&lt;br /&gt;
&lt;br /&gt;
DiVA url: [http://www.diva-portal.org/smash/record.jsf?pid=diva2:735193]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 160%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;Created By&amp;#039;&amp;#039;&amp;#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
----------------------------------------------------&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
[http://islab.hh.se/mediawiki/Siddhartha_Khandelwal Siddhartha Khandelwal]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
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&lt;br /&gt;
----&lt;/div&gt;</summary>
		<author><name>Siddhartha</name></author>
	</entry>
	<entry>
		<id>https://mw.hh.se/caisr/index.php?title=Gait_events&amp;diff=3392</id>
		<title>Gait events</title>
		<link rel="alternate" type="text/html" href="https://mw.hh.se/caisr/index.php?title=Gait_events&amp;diff=3392"/>
		<updated>2016-12-12T13:10:00Z</updated>

		<summary type="html">&lt;p&gt;Siddhartha: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;&lt;br /&gt;
== Gait Event Detection in Real-World Environments for Long-Term Applications ==&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
Detecting gait events is the key to many gait analysis applications that would benefit from continuous monitoring or long-term analysis. Most gait event detection algorithms using wearable sensors that offer a potential for use in daily living have been developed from data collected in controlled indoor experiments. However, for real-word applications, it is essential that the analysis is carried out in humans’ natural environment; that involves different gait speeds, changing walking terrains, varying surface inclinations and regular turns among other factors. Existing domain knowledge in the form of principles or underlying fundamental gait relationships can be utilized to drive and support the data analysis in order to develop robust algorithms that can tackle real-world challenges in gait analysis. This paper presents a novel approach that exhibits how domain knowledge about human gait can be incorporated into time-frequency analysis to detect gait events from longterm accelerometer signals. The accuracy and robustness of the proposed algorithm are validated by experiments done in indoor and outdoor environments with approximately 93,600 gait events in total. The proposed algorithm exhibits consistently high performance scores across all datasets in both, indoor and outdoor environments.&lt;br /&gt;
&lt;br /&gt;
[[File:accPlacement.jpg|right|thumb|200px|caption|&amp;quot;Figure 1: Position and orientation of each accelerometer at the beginning of each experiment. The accelerometer at the right ankle is attached arbitrarily without any pre-defined orientation. The FSRs are instrumented into the shoe soles to collect the ground truth. The data from all sensors is sampled at a frequency of 128 Hz.&amp;quot;]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
==List of Specifications==&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
*&amp;#039;&amp;#039;&amp;#039;Activities&amp;#039;&amp;#039;&amp;#039;: Walking and running&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
*&amp;#039;&amp;#039;&amp;#039;Accelerometer&amp;#039;&amp;#039;&amp;#039;:&lt;br /&gt;
**&amp;#039;&amp;#039;&amp;#039;Placement of 3-axis Accelerometer&amp;#039;&amp;#039;&amp;#039;: Anywhere around the ankle in any orientation as shown in Figure 1. &lt;br /&gt;
**&amp;#039;&amp;#039;&amp;#039;Sensitivity of the Accelerometer&amp;#039;&amp;#039;&amp;#039;: (+-) 4g or more. Please also check if the accelerometer signal has saturated during intense activity such as running. &lt;br /&gt;
**&amp;#039;&amp;#039;&amp;#039;Sampling Frequency&amp;#039;&amp;#039;&amp;#039;: Preferred - 128 Hz [A Sampling frequency of 50Hz and above is acceptable.]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
*&amp;#039;&amp;#039;&amp;#039;Input data&amp;#039;&amp;#039;&amp;#039;: &lt;br /&gt;
**accX - accelerometer data from X - axis&lt;br /&gt;
**accY - accelerometer data from Y - axis&lt;br /&gt;
**accZ - accelerometer data from Z - axis&lt;br /&gt;
**&amp;#039;&amp;#039;&amp;#039;Input data format&amp;#039;&amp;#039;&amp;#039;: The accelerometer signals should be in &amp;#039;&amp;#039;&amp;#039;units of m/s^2&amp;#039;&amp;#039;&amp;#039; and need to be in .mat format [in Matlab file format]. &lt;br /&gt;
**&amp;#039;&amp;#039;&amp;#039;IMPORTANT NOTE: The data should consist of ONLY walking and running segments of the signal. Segments corresponding to inactivity or any other activity should be removed from the signals prior to running the implementation.&amp;#039;&amp;#039;&amp;#039;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
==Implementation==&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
The implementation of the algorithm is provided as a library that can be used to detect gait events from 3-axis accelerometer signals collected during walking or running. &lt;br /&gt;
&lt;br /&gt;
[[File:figure_gaitEvents.png|thumb|center|1000px|caption|&amp;quot;Figure 2: The magnitude of the resultant accelerometer signal along with the detected Heel-Strike and Toe-Off events&amp;quot;]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 120%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;The MATLAB code can be found here&amp;#039;&amp;#039;&amp;#039;: [https://github.com/sidkha99/GaitEventDetection.git Link to Github repository]&amp;lt;/div&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;code&amp;gt;&amp;#039;&amp;#039;&amp;#039;[HS,TO] = SK_gedAlgo(accX,accY,accZ,Fs,winSizeFactor,implement_type);&amp;#039;&amp;#039;&amp;#039;&amp;lt;/code&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;#039;&amp;#039;&amp;#039;Input Arguments:&amp;#039;&amp;#039;&amp;#039;&lt;br /&gt;
&lt;br /&gt;
*accX, accY, accZ [unit: m/s^2]- signals obtained from each individual axis of 3-axes accelerometer&lt;br /&gt;
*Fs [unit: Hz] - sampling frequency of the acceleration signal. Originally developed for 128 Hz&lt;br /&gt;
*winSizeFactor - size of the running window. Default value = 3. Vary this size from 2 to 6 to get better results or if code crashes.&lt;br /&gt;
*implement_type - To detect the gait event, two implementations are provided. Default value: &amp;#039;fit&amp;#039;&lt;br /&gt;
**&amp;#039;fit&amp;#039; - 2D Gaussian distribution fitting is done to estimate gait event (runs slow)&lt;br /&gt;
**&amp;#039;fast&amp;#039;- faster implementation to estimate the event. Warning: &amp;#039;fast&amp;#039; implementation might not give the best estimate of the gait event compared to gaussian fitting. It is not presented in the paper but is solely provided to estimate events faster without gaussian fitting.&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;#039;&amp;#039;&amp;#039;Output Arguments:&amp;#039;&amp;#039;&amp;#039;&lt;br /&gt;
&lt;br /&gt;
*HS - vector containing sample numbers of Heel-Strike occurrences&lt;br /&gt;
*TO - vector containing sample numbers of Toe-Off occurrences&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 160%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;References&amp;#039;&amp;#039;&amp;#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
----------------------------------------------------&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 120%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;Please remember to include an appropriate citation to acknowledge the use of library in all documents and papers that uses it:&amp;#039;&amp;#039;&amp;#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
----------------------------------------------------&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
[1] Siddhartha Khandelwal; Nicholas Wickström, &amp;quot;Gait Event Detection in Real-World Environment for Long-Term Applications: Incorporating Domain Knowledge into Time-Frequency Analysis,&amp;quot; in IEEE Transactions on Neural Systems and Rehabilitation Engineering , vol. 24, no. 12, pp.1363-1372, 2016&lt;br /&gt;
&lt;br /&gt;
IEEE url: [http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7423805&amp;amp;newsearch=true&amp;amp;queryText=siddhartha%20khandelwal]&lt;br /&gt;
DiVA url: [http://hh.diva-portal.org/smash/record.jsf?searchId=1&amp;amp;pid=diva2:909015]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
[2] Siddhartha Khandelwal; Nicholas Wickström, &amp;quot;Identification of Gait Events using Expert Knowledge and Continuous Wavelet Transform Analysis&amp;quot;, in BIOSIGNALS 2014, 7th International Conference on Bio-inspired Systems and Signal Processing, Angers, France, March 3-6, 2014 &lt;br /&gt;
&lt;br /&gt;
DiVA url: [http://hh.diva-portal.org/smash/record.jsf?searchId=1&amp;amp;pid=diva2:688909]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 120%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;You may also want to read:&amp;#039;&amp;#039;&amp;#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
&lt;br /&gt;
[3] Siddhartha Khandelwal, Nicholas Wickström, Evaluation of the performance of accelerometer-based gait event detection algorithms in different real-world scenarios using the MAREA gait database, Gait &amp;amp; Posture, Volume 51, January 2017, Pages 84-90, ISSN 0966-6362, http://dx.doi.org/10.1016/j.gaitpost.2016.09.023.&lt;br /&gt;
&lt;br /&gt;
Sciencedirect: [http://www.sciencedirect.com/science/article/pii/S0966636216305859]&lt;br /&gt;
Diva: [http://hh.diva-portal.org/smash/record.jsf?searchId=1&amp;amp;pid=diva2%3A999938&amp;amp;dswid=1416]&lt;br /&gt;
&lt;br /&gt;
Link to the MAREA gait database: [http://islab.hh.se/mediawiki/Gait_database MAREA gait databse]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 160%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;Created By&amp;#039;&amp;#039;&amp;#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
----------------------------------------------------&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
[http://islab.hh.se/mediawiki/Siddhartha_Khandelwal Siddhartha Khandelwal]&lt;/div&gt;</summary>
		<author><name>Siddhartha</name></author>
	</entry>
	<entry>
		<id>https://mw.hh.se/caisr/index.php?title=Gait_database&amp;diff=3391</id>
		<title>Gait database</title>
		<link rel="alternate" type="text/html" href="https://mw.hh.se/caisr/index.php?title=Gait_database&amp;diff=3391"/>
		<updated>2016-12-12T13:09:33Z</updated>

		<summary type="html">&lt;p&gt;Siddhartha: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== MAREA (&amp;#039;&amp;#039;&amp;#039;M&amp;#039;&amp;#039;&amp;#039;ovement &amp;#039;&amp;#039;&amp;#039;A&amp;#039;&amp;#039;&amp;#039;nalysis in &amp;#039;&amp;#039;&amp;#039;R&amp;#039;&amp;#039;&amp;#039;eal-world &amp;#039;&amp;#039;&amp;#039;E&amp;#039;&amp;#039;&amp;#039;nvironments using &amp;#039;&amp;#039;&amp;#039;A&amp;#039;&amp;#039;&amp;#039;ccelerometers) Gait Database ==&lt;br /&gt;
&lt;br /&gt;
[[File:accPlacement.jpg|right|thumb|200px|caption|&amp;quot;Figure 1: Position and orientation of each accelerometer at the beginning of each experiment. The accelerometer at the right ankle is attached arbitrarily without any pre-defined orientation. The FSRs are instrumented into the shoe soles to collect the ground truth. The data from all sensors is sampled at a frequency of 128 Hz.&amp;quot;]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
The &amp;#039;&amp;#039;&amp;#039;MAREA&amp;#039;&amp;#039;&amp;#039; gait database comprises of gait activities in different real-world environments as shown in the table below. 20 healthy adults (12 males and 8 females, average age: 33.4 +- 7 years, average mass: 73.2 +- 10.9 kg, average height: 172.6 +- 9.5 cm) participated in the study that was approved by the Ethical Review Board of Lund, Sweden. Each subject had a 3-axes Shimmer3 (Shimmer Research, Dublin, Ireland) accelerometer (+- 8g) attached to their waist, left wrist and left and right ankles using elastic bands and velcro straps. Figure 1 shows the position and orientation of each accelerometer at the beginning of each experiment. On the waist, the accelerometer X and Y axes were pointing to the lateral and downward direction, respectively. On the wrist and left ankle, the Z axis was pointing in the lateral direction while the Y axis was pointing downward and was aligned with the limb longitudinal axis. In order to simulate a lesser controlled scenario, the accelerometer on right ankle was positioned such that the Y axis was pointing downward but the Z axis was marginally disturbed such that it was not exactly perpendicular to the sagittal plane. The subjects were provided shoes that were instrumented with piezo-electric force sensitive resistors (FSRs), fixed at the extreme ends of the sole in order to provide the ground truth values for HS and TO. An external expansion board was used to synchronously collect the data from the FSRs on each foot and the respective ankle accelerometer, at a sampling frequency of 128Hz, and stored locally on the Shimmer3 microSD card. Due to the lack of a centralized data logger, the waist accelerometer was not in perfect synchronization with the ankle accelerometer.&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
{| class=&amp;quot;wikitable&amp;quot;&lt;br /&gt;
! style=&amp;quot;text-align: center;&amp;quot; | Subjects&lt;br /&gt;
! style=&amp;quot;text-align: center;&amp;quot; | Environment&lt;br /&gt;
! style=&amp;quot;text-align: center;&amp;quot; | Activity&lt;br /&gt;
! style=&amp;quot;text-align: center;&amp;quot; | Speed&lt;br /&gt;
! style=&amp;quot;text-align: center;&amp;quot; | Duration&lt;br /&gt;
! style=&amp;quot;text-align: center;&amp;quot; | Short Description&lt;br /&gt;
|-&lt;br /&gt;
| rowspan=&amp;quot;4&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | 11&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | Treadmill (flat)&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | Walk &amp;amp; run&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | 4km/hr - 8km/hr; increasing in steps&lt;br /&gt;
 of 0.4km/hr every minute&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | 10 min&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | Start walking and switch to &lt;br /&gt;
running at self-selected speed&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | Treadmill (slope)&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | Walk&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | Self-selected&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | 12 min&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | Treadmill is set to (5, 0, 10, 0, 15, 0) degree&lt;br /&gt;
inclinations with 2 mins at each angle&lt;br /&gt;
|-&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Indoor flat space&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Walk &amp;amp; run&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Self-selected&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | 6 min&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Start walking and switch &lt;br /&gt;
to running after 3 mins&lt;br /&gt;
|-&lt;br /&gt;
 |&lt;br /&gt;
|-&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | 9&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Outdoor street&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Walk &amp;amp; run&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Self-selected&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | 6 min&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Start walking and switch &lt;br /&gt;
to running after 3 mins&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
|}&lt;br /&gt;
&lt;br /&gt;
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==&amp;#039;&amp;#039;&amp;#039;Explanation of the database files&amp;#039;&amp;#039;&amp;#039; (download link below)==&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
=== 1. Activity Timings === &lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
There are two matrices provided, namely, &amp;#039;&amp;#039;&amp;#039;Indoor Experiment timings&amp;#039;&amp;#039;&amp;#039; and  &amp;#039;&amp;#039;&amp;#039;Outdoor Experiment timings&amp;#039;&amp;#039;&amp;#039; which contain the timing information of the activities and are included in a folder called Activity Timings.zip: &lt;br /&gt;
&lt;br /&gt;
[[File:ActivityTimingsGraph.png|right|thumb|500px|caption|&amp;quot;Example of how to use the sample numbers given in Indoor Experiment Timings matrix to extract desired activity data. This figure shows the entire Indoor data collected from accelerometer positioned at Right Ankle for Subject 5. &amp;quot;]]&lt;br /&gt;
&lt;br /&gt;
* &amp;#039;&amp;#039;&amp;#039;Indoor Experiment Timings&amp;#039;&amp;#039;&amp;#039;: This 11(rows) x 8(columns) matrix consists of &amp;#039;&amp;#039;&amp;#039;sample numbers&amp;#039;&amp;#039;&amp;#039; corresponding to the start and end of an activity, for a given subject, for the &amp;#039;&amp;#039;&amp;#039;Indoor Experiments&amp;#039;&amp;#039;&amp;#039;, i.e. &lt;br /&gt;
**Treadmill (flat)&lt;br /&gt;
**Treadmill (slope) &lt;br /&gt;
**Indoor flat space. &lt;br /&gt;
The figure below explains how to extract the sample nos. corresponding to an activity for a given subject, from the Indoor Experiment timings matrix.  &lt;br /&gt;
&lt;br /&gt;
[[File:Indoor Table.png|700px]]&lt;br /&gt;
&lt;br /&gt;
* &amp;#039;&amp;#039;&amp;#039;Outdoor Experiment Timings&amp;#039;&amp;#039;&amp;#039;: This 9(rows) x 3(columns) matrix consists of &amp;#039;&amp;#039;&amp;#039;sample numbers&amp;#039;&amp;#039;&amp;#039; corresponding to the start and end of an activity, for a given subject, for the &amp;#039;&amp;#039;&amp;#039;Outdoor Street Experiments&amp;#039;&amp;#039;&amp;#039;. The figure below explains how to extract the sample nos. corresponding to an activity for a given subject, from the Outdoor Experiment timings matrix.&lt;br /&gt;
&lt;br /&gt;
[[File:Outdoor Table.png|800px]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
=== 2. Subject Data === &lt;br /&gt;
&lt;br /&gt;
[[File:AccXYZ.png|right|thumb|400px|caption|&amp;quot;Figure 2: Example of the accelerometer data from the X, Y and Z axis for Sub5_RF&amp;quot;]]&lt;br /&gt;
&lt;br /&gt;
The Subject data files are provided in two formats, namely, .mat format (&amp;#039;&amp;#039;&amp;#039;Subject Data_mat format.zip&amp;#039;&amp;#039;&amp;#039;) and .txt format &amp;#039;&amp;#039;&amp;#039;Subject Data_txt format).zip&amp;#039;&amp;#039;&amp;#039;. The naming convention of the files is: Sub&amp;lt;number&amp;gt;_&amp;lt;position&amp;gt;, where:&lt;br /&gt;
*&amp;lt;number&amp;gt;: stands for Subject number and ranges from 1 to 20. &lt;br /&gt;
*&amp;lt;position&amp;gt;: stands for the position of the accelerometer on the body as shown in Figure 1. The positions are:&lt;br /&gt;
**LF - Left Ankle&lt;br /&gt;
**RF - Right Ankle&lt;br /&gt;
**Wrist&lt;br /&gt;
**Waist&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
For each Subject file, eg. Sub5_RF, the accelerometer data from the 3 axis accelerometer is stored in 3 columns (separated using comma in the .txt files), each named as: &lt;br /&gt;
*accX - data from X - axis&lt;br /&gt;
*accY - data from Y - axis&lt;br /&gt;
*accZ - data from Z - axis&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
===3. Ground Truth===&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
The Ground Truth is provided as a 11x7 structure that gives the timing of the Heel-Strike and Toe-Off events extracted from the FSR signals. This timing information is provided in terms of sample numbers and is relative to each activity. The structure consists of 7 fields that have &amp;#039;&amp;#039;&amp;#039;already been segregated&amp;#039;&amp;#039;&amp;#039; using the Indoor Experiment Timings and Outdoor Experiment Timings explained above: &lt;br /&gt;
*treadWalk       - treadmill (flat) walk &lt;br /&gt;
&lt;br /&gt;
*treadIncline    - treadmill (slope) walk&lt;br /&gt;
&lt;br /&gt;
*treadWalknRun   - treadmill (flat) walk &amp;amp; run&lt;br /&gt;
&lt;br /&gt;
*indoorWalk      - indoor (flat space) walk&lt;br /&gt;
&lt;br /&gt;
*indoorWalknRun  - indoor (flat space) walk &amp;amp; run&lt;br /&gt;
&lt;br /&gt;
*outdoorWalk     - outdoor street walk&lt;br /&gt;
&lt;br /&gt;
*outdoorWalknRun - outdoor street walk &amp;amp; run &lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
Each row of the structure represents a Subject. For Indoor activities, Row 1 -&amp;gt; Subject1, ..., Row 11 -&amp;gt; Subject 11 and so on. For outdoor activities, Row 1 -&amp;gt; Subject 12,..., Row 9 -&amp;gt; Subject 20. As an example: GroundTruth(1).treadWalk provides the ground truth data for Subject 1 for treadmill (flat) walk. This is a structure with fields: &lt;br /&gt;
* SubIdx - Subject number. Ranges from Sub1 - Sub20&lt;br /&gt;
&lt;br /&gt;
* LF_HS - Sample numbers of Heel-Strike event from Left Foot FSR signal.&lt;br /&gt;
&lt;br /&gt;
* LF_TO - Sample numbers of Toe-Off event from Left Foot FSR signal.&lt;br /&gt;
&lt;br /&gt;
* RF_HS - Sample numbers of Heel-Strike event  from Right Foot FSR signal. &lt;br /&gt;
&lt;br /&gt;
* RF_TO - Sample numbers of Toe-Off event from Right Foot FSR signal.&lt;br /&gt;
&lt;br /&gt;
[[File:GroundTruthStructure.png|1200px]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
=== 4. mainScript.m === &lt;br /&gt;
&lt;br /&gt;
This MATLAB script is an example on how to:&lt;br /&gt;
&lt;br /&gt;
* Read the accelerometer data of a subject for an activity &amp;amp; position&lt;br /&gt;
&lt;br /&gt;
* Read the Ground Truth Heel-Strike and Toe-Off gait events for the same&lt;br /&gt;
&lt;br /&gt;
* Plot all the signals&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
----&lt;br /&gt;
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&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 160%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;How to get the datasets?&amp;#039;&amp;#039;&amp;#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
&lt;br /&gt;
----------------------------------------------------------------------------------------------------------------------------------&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 110%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;1. Link to the Release Agreement and fill Registration Form:&amp;#039;&amp;#039;&amp;#039;  [http://islab.hh.se/mediawiki/Gait_database:_Release_agreement Click_Release Agreement]&amp;lt;/div&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 110%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;2. Link to download the datasets:&amp;#039;&amp;#039;&amp;#039;  Will be sent by Email after Registration Procedure is completed &amp;lt;/div&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 110%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;2. Link to the code for gait event detection algorithm:&amp;#039;&amp;#039;&amp;#039;  [http://islab.hh.se/mediawiki/Gait_events Gait Events]&amp;lt;/div&amp;gt;&lt;br /&gt;
&lt;br /&gt;
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&amp;lt;div style=&amp;quot;font-size: 160%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;References&amp;#039;&amp;#039;&amp;#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
----------------------------------------------------&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 120%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;Please remember to include an appropriate citation to acknowledge the use of the database in all documents and papers that uses the MAREA Gait Database:&amp;#039;&amp;#039;&amp;#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
1. Siddhartha Khandelwal, Nicholas Wickström, Evaluation of the performance of accelerometer-based gait event detection algorithms in different real-world scenarios using the MAREA gait database, Gait &amp;amp; Posture, Volume 51, January 2017, Pages 84-90, ISSN 0966-6362, http://dx.doi.org/10.1016/j.gaitpost.2016.09.023.&lt;br /&gt;
&lt;br /&gt;
Sciencedirect: [http://www.sciencedirect.com/science/article/pii/S0966636216305859]&lt;br /&gt;
Diva: [http://hh.diva-portal.org/smash/record.jsf?searchId=1&amp;amp;pid=diva2%3A999938&amp;amp;dswid=1416]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
2. Siddhartha Khandelwal; Nicholas Wickström, &amp;quot;Gait Event Detection in Real-World Environment for Long-Term Applications: Incorporating Domain Knowledge into Time-Frequency Analysis,&amp;quot; in IEEE Transactions on Neural Systems and Rehabilitation Engineering , vol. 24, no. 12, pp.1363-1372, 2016 &lt;br /&gt;
&lt;br /&gt;
IEEE url: [http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7423805&amp;amp;newsearch=true&amp;amp;queryText=siddhartha%20khandelwal]&lt;br /&gt;
DiVA url: [http://hh.diva-portal.org/smash/record.jsf?searchId=1&amp;amp;pid=diva2:909015]&lt;br /&gt;
&lt;br /&gt;
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&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 120%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;You may also want to read:&amp;#039;&amp;#039;&amp;#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
3. Siddhartha Khandelwal; Nicholas Wickström, &amp;quot;Identification of Gait Events using Expert Knowledge and Continuous Wavelet Transform Analysis&amp;quot;, in BIOSIGNALS 2014, 7th International Conference on Bio-inspired Systems and Signal Processing, Angers, France, March 3-6, 2014 &lt;br /&gt;
&lt;br /&gt;
DiVA url: [http://hh.diva-portal.org/smash/record.jsf?searchId=1&amp;amp;pid=diva2:688909]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
4. Siddhartha Khandelwal, Nicholas Wickström, &amp;quot;Detecting Gait Events from Outdoor Accelerometer Data for Long-term and Continuous Monitoring Applications&amp;quot; in 13th International Symposium on 3D Analysis of Human Movement (3D-AHM 2014), 14–17 July, 2014, Lausanne, Switzerland.&lt;br /&gt;
&lt;br /&gt;
DiVA url: [http://www.diva-portal.org/smash/record.jsf?pid=diva2:735193]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 160%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;Created By&amp;#039;&amp;#039;&amp;#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
----------------------------------------------------&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
[http://islab.hh.se/mediawiki/Siddhartha_Khandelwal Siddhartha Khandelwal]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
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----&lt;/div&gt;</summary>
		<author><name>Siddhartha</name></author>
	</entry>
	<entry>
		<id>https://mw.hh.se/caisr/index.php?title=Gait_events&amp;diff=3270</id>
		<title>Gait events</title>
		<link rel="alternate" type="text/html" href="https://mw.hh.se/caisr/index.php?title=Gait_events&amp;diff=3270"/>
		<updated>2016-10-24T21:13:04Z</updated>

		<summary type="html">&lt;p&gt;Siddhartha: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;&lt;br /&gt;
== Gait Event Detection in Real-World Environments for Long-Term Applications ==&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
Detecting gait events is the key to many gait analysis applications that would benefit from continuous monitoring or long-term analysis. Most gait event detection algorithms using wearable sensors that offer a potential for use in daily living have been developed from data collected in controlled indoor experiments. However, for real-word applications, it is essential that the analysis is carried out in humans’ natural environment; that involves different gait speeds, changing walking terrains, varying surface inclinations and regular turns among other factors. Existing domain knowledge in the form of principles or underlying fundamental gait relationships can be utilized to drive and support the data analysis in order to develop robust algorithms that can tackle real-world challenges in gait analysis. This paper presents a novel approach that exhibits how domain knowledge about human gait can be incorporated into time-frequency analysis to detect gait events from longterm accelerometer signals. The accuracy and robustness of the proposed algorithm are validated by experiments done in indoor and outdoor environments with approximately 93,600 gait events in total. The proposed algorithm exhibits consistently high performance scores across all datasets in both, indoor and outdoor environments.&lt;br /&gt;
&lt;br /&gt;
[[File:accPlacement.jpg|right|thumb|200px|caption|&amp;quot;Figure 1: Position and orientation of each accelerometer at the beginning of each experiment. The accelerometer at the right ankle is attached arbitrarily without any pre-defined orientation. The FSRs are instrumented into the shoe soles to collect the ground truth. The data from all sensors is sampled at a frequency of 128 Hz.&amp;quot;]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
==List of Specifications==&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
*&amp;#039;&amp;#039;&amp;#039;Activities&amp;#039;&amp;#039;&amp;#039;: Walking and running&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
*&amp;#039;&amp;#039;&amp;#039;Accelerometer&amp;#039;&amp;#039;&amp;#039;:&lt;br /&gt;
**&amp;#039;&amp;#039;&amp;#039;Placement of 3-axis Accelerometer&amp;#039;&amp;#039;&amp;#039;: Anywhere around the ankle in any orientation as shown in Figure 1. &lt;br /&gt;
**&amp;#039;&amp;#039;&amp;#039;Sensitivity of the Accelerometer&amp;#039;&amp;#039;&amp;#039;: (+-) 4g or more. Please also check if the accelerometer signal has saturated during intense activity such as running. &lt;br /&gt;
**&amp;#039;&amp;#039;&amp;#039;Sampling Frequency&amp;#039;&amp;#039;&amp;#039;: Preferred - 128 Hz [A Sampling frequency of 50Hz and above is acceptable.]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
*&amp;#039;&amp;#039;&amp;#039;Input data&amp;#039;&amp;#039;&amp;#039;: &lt;br /&gt;
**accX - accelerometer data from X - axis&lt;br /&gt;
**accY - accelerometer data from Y - axis&lt;br /&gt;
**accZ - accelerometer data from Z - axis&lt;br /&gt;
**&amp;#039;&amp;#039;&amp;#039;Input data format&amp;#039;&amp;#039;&amp;#039;: The accelerometer signals should be in &amp;#039;&amp;#039;&amp;#039;units of m/s^2&amp;#039;&amp;#039;&amp;#039; and need to be in .mat format [in Matlab file format]. &lt;br /&gt;
**&amp;#039;&amp;#039;&amp;#039;IMPORTANT NOTE: The data should consist of ONLY walking and running segments of the signal. Segments corresponding to inactivity or any other activity should be removed from the signals prior to running the implementation.&amp;#039;&amp;#039;&amp;#039;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
==Implementation==&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
The implementation of the algorithm is provided as a library that can be used to detect gait events from 3-axis accelerometer signals collected during walking or running. &lt;br /&gt;
&lt;br /&gt;
[[File:figure_gaitEvents.png|thumb|center|1000px|caption|&amp;quot;Figure 2: The magnitude of the resultant accelerometer signal along with the detected Heel-Strike and Toe-Off events&amp;quot;]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 120%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;The MATLAB code can be found here&amp;#039;&amp;#039;&amp;#039;: [https://github.com/sidkha99/GaitEventDetection.git Link to Github repository]&amp;lt;/div&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;code&amp;gt;&amp;#039;&amp;#039;&amp;#039;[HS,TO] = SK_gedAlgo(accX,accY,accZ,Fs,winSizeFactor,implement_type);&amp;#039;&amp;#039;&amp;#039;&amp;lt;/code&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;#039;&amp;#039;&amp;#039;Input Arguments:&amp;#039;&amp;#039;&amp;#039;&lt;br /&gt;
&lt;br /&gt;
*accX, accY, accZ [unit: m/s^2]- signals obtained from each individual axis of 3-axes accelerometer&lt;br /&gt;
*Fs [unit: Hz] - sampling frequency of the acceleration signal. Originally developed for 128 Hz&lt;br /&gt;
*winSizeFactor - size of the running window. Default value = 3. Vary this size from 2 to 6 to get better results or if code crashes.&lt;br /&gt;
*implement_type - To detect the gait event, two implementations are provided. Default value: &amp;#039;fit&amp;#039;&lt;br /&gt;
**&amp;#039;fit&amp;#039; - 2D Gaussian distribution fitting is done to estimate gait event (runs slow)&lt;br /&gt;
**&amp;#039;fast&amp;#039;- faster implementation to estimate the event. Warning: &amp;#039;fast&amp;#039; implementation might not give the best estimate of the gait event compared to gaussian fitting. It is not presented in the paper but is solely provided to estimate events faster without gaussian fitting.&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;#039;&amp;#039;&amp;#039;Output Arguments:&amp;#039;&amp;#039;&amp;#039;&lt;br /&gt;
&lt;br /&gt;
*HS - vector containing sample numbers of Heel-Strike occurrences&lt;br /&gt;
*TO - vector containing sample numbers of Toe-Off occurrences&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 160%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;References&amp;#039;&amp;#039;&amp;#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
----------------------------------------------------&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 120%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;Please remember to include an appropriate citation to acknowledge the use of library in all documents and papers that uses it:&amp;#039;&amp;#039;&amp;#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
----------------------------------------------------&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
[1] Siddhartha Khandelwal; Nicholas Wickström, &amp;quot;Gait Event Detection in Real-World Environment for Long-Term Applications: Incorporating Domain Knowledge into Time-Frequency Analysis,&amp;quot; in IEEE Transactions on Neural Systems and Rehabilitation Engineering , vol.PP, no.99, pp.1-1 &lt;br /&gt;
&lt;br /&gt;
IEEE url: [http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7423805&amp;amp;newsearch=true&amp;amp;queryText=siddhartha%20khandelwal]&lt;br /&gt;
DiVA url: [http://hh.diva-portal.org/smash/record.jsf?searchId=1&amp;amp;pid=diva2:909015]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
[2] Siddhartha Khandelwal; Nicholas Wickström, &amp;quot;Identification of Gait Events using Expert Knowledge and Continuous Wavelet Transform Analysis&amp;quot;, in BIOSIGNALS 2014, 7th International Conference on Bio-inspired Systems and Signal Processing, Angers, France, March 3-6, 2014 &lt;br /&gt;
&lt;br /&gt;
DiVA url: [http://hh.diva-portal.org/smash/record.jsf?searchId=1&amp;amp;pid=diva2:688909]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 120%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;You may also want to read:&amp;#039;&amp;#039;&amp;#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
&lt;br /&gt;
[3] Siddhartha Khandelwal, Nicholas Wickström, Evaluation of the performance of accelerometer-based gait event detection algorithms in different real-world scenarios using the MAREA gait database, Gait &amp;amp; Posture, Volume 51, January 2017, Pages 84-90, ISSN 0966-6362, http://dx.doi.org/10.1016/j.gaitpost.2016.09.023.&lt;br /&gt;
&lt;br /&gt;
Sciencedirect: [http://www.sciencedirect.com/science/article/pii/S0966636216305859]&lt;br /&gt;
Diva: [http://hh.diva-portal.org/smash/record.jsf?searchId=1&amp;amp;pid=diva2%3A999938&amp;amp;dswid=1416]&lt;br /&gt;
&lt;br /&gt;
Link to the MAREA gait database: [http://islab.hh.se/mediawiki/Gait_database MAREA gait databse]&lt;br /&gt;
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&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 160%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;Created By&amp;#039;&amp;#039;&amp;#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
----------------------------------------------------&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
[http://islab.hh.se/mediawiki/Siddhartha_Khandelwal Siddhartha Khandelwal]&lt;/div&gt;</summary>
		<author><name>Siddhartha</name></author>
	</entry>
	<entry>
		<id>https://mw.hh.se/caisr/index.php?title=Gait_database&amp;diff=3269</id>
		<title>Gait database</title>
		<link rel="alternate" type="text/html" href="https://mw.hh.se/caisr/index.php?title=Gait_database&amp;diff=3269"/>
		<updated>2016-10-24T19:27:42Z</updated>

		<summary type="html">&lt;p&gt;Siddhartha: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== MAREA (&amp;#039;&amp;#039;&amp;#039;M&amp;#039;&amp;#039;&amp;#039;ovement &amp;#039;&amp;#039;&amp;#039;A&amp;#039;&amp;#039;&amp;#039;nalysis in &amp;#039;&amp;#039;&amp;#039;R&amp;#039;&amp;#039;&amp;#039;eal-world &amp;#039;&amp;#039;&amp;#039;E&amp;#039;&amp;#039;&amp;#039;nvironments using &amp;#039;&amp;#039;&amp;#039;A&amp;#039;&amp;#039;&amp;#039;ccelerometers) Gait Database ==&lt;br /&gt;
&lt;br /&gt;
[[File:accPlacement.jpg|right|thumb|200px|caption|&amp;quot;Figure 1: Position and orientation of each accelerometer at the beginning of each experiment. The accelerometer at the right ankle is attached arbitrarily without any pre-defined orientation. The FSRs are instrumented into the shoe soles to collect the ground truth. The data from all sensors is sampled at a frequency of 128 Hz.&amp;quot;]]&lt;br /&gt;
&lt;br /&gt;
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The &amp;#039;&amp;#039;&amp;#039;MAREA&amp;#039;&amp;#039;&amp;#039; gait database comprises of gait activities in different real-world environments as shown in the table below. 20 healthy adults (12 males and 8 females, average age: 33.4 +- 7 years, average mass: 73.2 +- 10.9 kg, average height: 172.6 +- 9.5 cm) participated in the study that was approved by the Ethical Review Board of Lund, Sweden. Each subject had a 3-axes Shimmer3 (Shimmer Research, Dublin, Ireland) accelerometer (+- 8g) attached to their waist, left wrist and left and right ankles using elastic bands and velcro straps. Figure 1 shows the position and orientation of each accelerometer at the beginning of each experiment. On the waist, the accelerometer X and Y axes were pointing to the lateral and downward direction, respectively. On the wrist and left ankle, the Z axis was pointing in the lateral direction while the Y axis was pointing downward and was aligned with the limb longitudinal axis. In order to simulate a lesser controlled scenario, the accelerometer on right ankle was positioned such that the Y axis was pointing downward but the Z axis was marginally disturbed such that it was not exactly perpendicular to the sagittal plane. The subjects were provided shoes that were instrumented with piezo-electric force sensitive resistors (FSRs), fixed at the extreme ends of the sole in order to provide the ground truth values for HS and TO. An external expansion board was used to synchronously collect the data from the FSRs on each foot and the respective ankle accelerometer, at a sampling frequency of 128Hz, and stored locally on the Shimmer3 microSD card. Due to the lack of a centralized data logger, the waist accelerometer was not in perfect synchronization with the ankle accelerometer.&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
{| class=&amp;quot;wikitable&amp;quot;&lt;br /&gt;
! style=&amp;quot;text-align: center;&amp;quot; | Subjects&lt;br /&gt;
! style=&amp;quot;text-align: center;&amp;quot; | Environment&lt;br /&gt;
! style=&amp;quot;text-align: center;&amp;quot; | Activity&lt;br /&gt;
! style=&amp;quot;text-align: center;&amp;quot; | Speed&lt;br /&gt;
! style=&amp;quot;text-align: center;&amp;quot; | Duration&lt;br /&gt;
! style=&amp;quot;text-align: center;&amp;quot; | Short Description&lt;br /&gt;
|-&lt;br /&gt;
| rowspan=&amp;quot;4&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | 11&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | Treadmill (flat)&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | Walk &amp;amp; run&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | 4km/hr - 8km/hr; increasing in steps&lt;br /&gt;
 of 0.4km/hr every minute&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | 10 min&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | Start walking and switch to &lt;br /&gt;
running at self-selected speed&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | Treadmill (slope)&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | Walk&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | Self-selected&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | 12 min&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | Treadmill is set to (5, 0, 10, 0, 15, 0) degree&lt;br /&gt;
inclinations with 2 mins at each angle&lt;br /&gt;
|-&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Indoor flat space&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Walk &amp;amp; run&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Self-selected&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | 6 min&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Start walking and switch &lt;br /&gt;
to running after 3 mins&lt;br /&gt;
|-&lt;br /&gt;
 |&lt;br /&gt;
|-&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | 9&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Outdoor street&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Walk &amp;amp; run&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Self-selected&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | 6 min&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Start walking and switch &lt;br /&gt;
to running after 3 mins&lt;br /&gt;
|-&lt;br /&gt;
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|}&lt;br /&gt;
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==&amp;#039;&amp;#039;&amp;#039;Explanation of the database files&amp;#039;&amp;#039;&amp;#039; (download link below)==&lt;br /&gt;
&lt;br /&gt;
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=== 1. Activity Timings === &lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
There are two matrices provided, namely, &amp;#039;&amp;#039;&amp;#039;Indoor Experiment timings&amp;#039;&amp;#039;&amp;#039; and  &amp;#039;&amp;#039;&amp;#039;Outdoor Experiment timings&amp;#039;&amp;#039;&amp;#039; which contain the timing information of the activities and are included in a folder called Activity Timings.zip: &lt;br /&gt;
&lt;br /&gt;
[[File:ActivityTimingsGraph.png|right|thumb|500px|caption|&amp;quot;Example of how to use the sample numbers given in Indoor Experiment Timings matrix to extract desired activity data. This figure shows the entire Indoor data collected from accelerometer positioned at Right Ankle for Subject 5. &amp;quot;]]&lt;br /&gt;
&lt;br /&gt;
* &amp;#039;&amp;#039;&amp;#039;Indoor Experiment Timings&amp;#039;&amp;#039;&amp;#039;: This 11(rows) x 8(columns) matrix consists of &amp;#039;&amp;#039;&amp;#039;sample numbers&amp;#039;&amp;#039;&amp;#039; corresponding to the start and end of an activity, for a given subject, for the &amp;#039;&amp;#039;&amp;#039;Indoor Experiments&amp;#039;&amp;#039;&amp;#039;, i.e. &lt;br /&gt;
**Treadmill (flat)&lt;br /&gt;
**Treadmill (slope) &lt;br /&gt;
**Indoor flat space. &lt;br /&gt;
The figure below explains how to extract the sample nos. corresponding to an activity for a given subject, from the Indoor Experiment timings matrix.  &lt;br /&gt;
&lt;br /&gt;
[[File:Indoor Table.png|700px]]&lt;br /&gt;
&lt;br /&gt;
* &amp;#039;&amp;#039;&amp;#039;Outdoor Experiment Timings&amp;#039;&amp;#039;&amp;#039;: This 9(rows) x 3(columns) matrix consists of &amp;#039;&amp;#039;&amp;#039;sample numbers&amp;#039;&amp;#039;&amp;#039; corresponding to the start and end of an activity, for a given subject, for the &amp;#039;&amp;#039;&amp;#039;Outdoor Street Experiments&amp;#039;&amp;#039;&amp;#039;. The figure below explains how to extract the sample nos. corresponding to an activity for a given subject, from the Outdoor Experiment timings matrix.&lt;br /&gt;
&lt;br /&gt;
[[File:Outdoor Table.png|800px]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
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=== 2. Subject Data === &lt;br /&gt;
&lt;br /&gt;
[[File:AccXYZ.png|right|thumb|400px|caption|&amp;quot;Figure 2: Example of the accelerometer data from the X, Y and Z axis for Sub5_RF&amp;quot;]]&lt;br /&gt;
&lt;br /&gt;
The Subject data files are provided in two formats, namely, .mat format (&amp;#039;&amp;#039;&amp;#039;Subject Data_mat format.zip&amp;#039;&amp;#039;&amp;#039;) and .txt format &amp;#039;&amp;#039;&amp;#039;Subject Data_txt format).zip&amp;#039;&amp;#039;&amp;#039;. The naming convention of the files is: Sub&amp;lt;number&amp;gt;_&amp;lt;position&amp;gt;, where:&lt;br /&gt;
*&amp;lt;number&amp;gt;: stands for Subject number and ranges from 1 to 20. &lt;br /&gt;
*&amp;lt;position&amp;gt;: stands for the position of the accelerometer on the body as shown in Figure 1. The positions are:&lt;br /&gt;
**LF - Left Ankle&lt;br /&gt;
**RF - Right Ankle&lt;br /&gt;
**Wrist&lt;br /&gt;
**Waist&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
For each Subject file, eg. Sub5_RF, the accelerometer data from the 3 axis accelerometer is stored in 3 columns (separated using comma in the .txt files), each named as: &lt;br /&gt;
*accX - data from X - axis&lt;br /&gt;
*accY - data from Y - axis&lt;br /&gt;
*accZ - data from Z - axis&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
===3. Ground Truth===&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
The Ground Truth is provided as a 11x7 structure that gives the timing of the Heel-Strike and Toe-Off events extracted from the FSR signals. This timing information is provided in terms of sample numbers and is relative to each activity. The structure consists of 7 fields that have &amp;#039;&amp;#039;&amp;#039;already been segregated&amp;#039;&amp;#039;&amp;#039; using the Indoor Experiment Timings and Outdoor Experiment Timings explained above: &lt;br /&gt;
*treadWalk       - treadmill (flat) walk &lt;br /&gt;
&lt;br /&gt;
*treadIncline    - treadmill (slope) walk&lt;br /&gt;
&lt;br /&gt;
*treadWalknRun   - treadmill (flat) walk &amp;amp; run&lt;br /&gt;
&lt;br /&gt;
*indoorWalk      - indoor (flat space) walk&lt;br /&gt;
&lt;br /&gt;
*indoorWalknRun  - indoor (flat space) walk &amp;amp; run&lt;br /&gt;
&lt;br /&gt;
*outdoorWalk     - outdoor street walk&lt;br /&gt;
&lt;br /&gt;
*outdoorWalknRun - outdoor street walk &amp;amp; run &lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
Each row of the structure represents a Subject. For Indoor activities, Row 1 -&amp;gt; Subject1, ..., Row 11 -&amp;gt; Subject 11 and so on. For outdoor activities, Row 1 -&amp;gt; Subject 12,..., Row 9 -&amp;gt; Subject 20. As an example: GroundTruth(1).treadWalk provides the ground truth data for Subject 1 for treadmill (flat) walk. This is a structure with fields: &lt;br /&gt;
* SubIdx - Subject number. Ranges from Sub1 - Sub20&lt;br /&gt;
&lt;br /&gt;
* LF_HS - Sample numbers of Heel-Strike event from Left Foot FSR signal.&lt;br /&gt;
&lt;br /&gt;
* LF_TO - Sample numbers of Toe-Off event from Left Foot FSR signal.&lt;br /&gt;
&lt;br /&gt;
* RF_HS - Sample numbers of Heel-Strike event  from Right Foot FSR signal. &lt;br /&gt;
&lt;br /&gt;
* RF_TO - Sample numbers of Toe-Off event from Right Foot FSR signal.&lt;br /&gt;
&lt;br /&gt;
[[File:GroundTruthStructure.png|1200px]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
=== 4. mainScript.m === &lt;br /&gt;
&lt;br /&gt;
This MATLAB script is an example on how to:&lt;br /&gt;
&lt;br /&gt;
* Read the accelerometer data of a subject for an activity &amp;amp; position&lt;br /&gt;
&lt;br /&gt;
* Read the Ground Truth Heel-Strike and Toe-Off gait events for the same&lt;br /&gt;
&lt;br /&gt;
* Plot all the signals&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
----&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 160%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;How to get the datasets?&amp;#039;&amp;#039;&amp;#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
&lt;br /&gt;
----------------------------------------------------------------------------------------------------------------------------------&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 110%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;1. Link to the Release Agreement and fill Registration Form:&amp;#039;&amp;#039;&amp;#039;  [http://islab.hh.se/mediawiki/Gait_database:_Release_agreement Click_Release Agreement]&amp;lt;/div&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 110%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;2. Link to download the datasets:&amp;#039;&amp;#039;&amp;#039;  Will be sent by Email after Registration Procedure is completed &amp;lt;/div&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 110%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;2. Link to the code for gait event detection algorithm:&amp;#039;&amp;#039;&amp;#039;  [http://islab.hh.se/mediawiki/Gait_events Gait Events]&amp;lt;/div&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 160%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;References&amp;#039;&amp;#039;&amp;#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
----------------------------------------------------&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 120%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;Please remember to include an appropriate citation to acknowledge the use of the database in all documents and papers that uses the MAREA Gait Database:&amp;#039;&amp;#039;&amp;#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
1. Siddhartha Khandelwal, Nicholas Wickström, Evaluation of the performance of accelerometer-based gait event detection algorithms in different real-world scenarios using the MAREA gait database, Gait &amp;amp; Posture, Volume 51, January 2017, Pages 84-90, ISSN 0966-6362, http://dx.doi.org/10.1016/j.gaitpost.2016.09.023.&lt;br /&gt;
&lt;br /&gt;
Sciencedirect: [http://www.sciencedirect.com/science/article/pii/S0966636216305859]&lt;br /&gt;
Diva: [http://hh.diva-portal.org/smash/record.jsf?searchId=1&amp;amp;pid=diva2%3A999938&amp;amp;dswid=1416]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
2. Siddhartha Khandelwal; Nicholas Wickström, &amp;quot;Gait Event Detection in Real-World Environment for Long-Term Applications: Incorporating Domain Knowledge into Time-Frequency Analysis,&amp;quot; in IEEE Transactions on Neural Systems and Rehabilitation Engineering , vol.PP, no.99, pp.1-1, 2016 &lt;br /&gt;
&lt;br /&gt;
IEEE url: [http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7423805&amp;amp;newsearch=true&amp;amp;queryText=siddhartha%20khandelwal]&lt;br /&gt;
DiVA url: [http://hh.diva-portal.org/smash/record.jsf?searchId=1&amp;amp;pid=diva2:909015]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 120%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;You may also want to read:&amp;#039;&amp;#039;&amp;#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
3. Siddhartha Khandelwal; Nicholas Wickström, &amp;quot;Identification of Gait Events using Expert Knowledge and Continuous Wavelet Transform Analysis&amp;quot;, in BIOSIGNALS 2014, 7th International Conference on Bio-inspired Systems and Signal Processing, Angers, France, March 3-6, 2014 &lt;br /&gt;
&lt;br /&gt;
DiVA url: [http://hh.diva-portal.org/smash/record.jsf?searchId=1&amp;amp;pid=diva2:688909]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
4. Siddhartha Khandelwal, Nicholas Wickström, &amp;quot;Detecting Gait Events from Outdoor Accelerometer Data for Long-term and Continuous Monitoring Applications&amp;quot; in 13th International Symposium on 3D Analysis of Human Movement (3D-AHM 2014), 14–17 July, 2014, Lausanne, Switzerland.&lt;br /&gt;
&lt;br /&gt;
DiVA url: [http://www.diva-portal.org/smash/record.jsf?pid=diva2:735193]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 160%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;Created By&amp;#039;&amp;#039;&amp;#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
----------------------------------------------------&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
[http://islab.hh.se/mediawiki/Siddhartha_Khandelwal Siddhartha Khandelwal]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
----&lt;/div&gt;</summary>
		<author><name>Siddhartha</name></author>
	</entry>
	<entry>
		<id>https://mw.hh.se/caisr/index.php?title=Gait_events&amp;diff=3267</id>
		<title>Gait events</title>
		<link rel="alternate" type="text/html" href="https://mw.hh.se/caisr/index.php?title=Gait_events&amp;diff=3267"/>
		<updated>2016-10-24T16:35:19Z</updated>

		<summary type="html">&lt;p&gt;Siddhartha: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;&lt;br /&gt;
== Gait Event Detection in Real-World Environments for Long-Term Applications ==&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
Detecting gait events is the key to many gait analysis applications that would benefit from continuous monitoring or long-term analysis. Most gait event detection algorithms using wearable sensors that offer a potential for use in daily living have been developed from data collected in controlled indoor experiments. However, for real-word applications, it is essential that the analysis is carried out in humans’ natural environment; that involves different gait speeds, changing walking terrains, varying surface inclinations and regular turns among other factors. Existing domain knowledge in the form of principles or underlying fundamental gait relationships can be utilized to drive and support the data analysis in order to develop robust algorithms that can tackle real-world challenges in gait analysis. This paper presents a novel approach that exhibits how domain knowledge about human gait can be incorporated into time-frequency analysis to detect gait events from longterm accelerometer signals. The accuracy and robustness of the proposed algorithm are validated by experiments done in indoor and outdoor environments with approximately 93,600 gait events in total. The proposed algorithm exhibits consistently high performance scores across all datasets in both, indoor and outdoor environments.&lt;br /&gt;
&lt;br /&gt;
[[File:accPlacement.jpg|right|thumb|200px|caption|&amp;quot;Figure 1: Position and orientation of each accelerometer at the beginning of each experiment. The accelerometer at the right ankle is attached arbitrarily without any pre-defined orientation. The FSRs are instrumented into the shoe soles to collect the ground truth. The data from all sensors is sampled at a frequency of 128 Hz.&amp;quot;]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
==List of Specifications==&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
*&amp;#039;&amp;#039;&amp;#039;Activities&amp;#039;&amp;#039;&amp;#039;: Walking and running&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
*&amp;#039;&amp;#039;&amp;#039;Accelerometer&amp;#039;&amp;#039;&amp;#039;:&lt;br /&gt;
**&amp;#039;&amp;#039;&amp;#039;Placement of 3-axis Accelerometer&amp;#039;&amp;#039;&amp;#039;: Anywhere around the ankle in any orientation as shown in Figure 1. &lt;br /&gt;
**&amp;#039;&amp;#039;&amp;#039;Sensitivity of the Accelerometer&amp;#039;&amp;#039;&amp;#039;: (+-) 4g or more. Please also check if the accelerometer signal has saturated during intense activity such as running. &lt;br /&gt;
**&amp;#039;&amp;#039;&amp;#039;Sampling Frequency&amp;#039;&amp;#039;&amp;#039;: Preferred - 128 Hz [A Sampling frequency of 50Hz and above is acceptable.]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
*&amp;#039;&amp;#039;&amp;#039;Input data&amp;#039;&amp;#039;&amp;#039;: &lt;br /&gt;
**accX - accelerometer data from X - axis&lt;br /&gt;
**accY - accelerometer data from Y - axis&lt;br /&gt;
**accZ - accelerometer data from Z - axis&lt;br /&gt;
**&amp;#039;&amp;#039;&amp;#039;Input data format&amp;#039;&amp;#039;&amp;#039;: The accelerometer signals should be in &amp;#039;&amp;#039;&amp;#039;units of m/s^2&amp;#039;&amp;#039;&amp;#039; and need to be in .mat format [in Matlab file format]. &lt;br /&gt;
**&amp;#039;&amp;#039;&amp;#039;IMPORTANT NOTE: The data should consist of ONLY walking and running segments of the signal. Segments corresponding to inactivity or any other activity should be removed from the signals prior to running the implementation.&amp;#039;&amp;#039;&amp;#039;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
==Implementation==&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
The implementation of the algorithm is provided as a library that can be used to detect gait events from 3-axis accelerometer signals collected during walking or running. &lt;br /&gt;
&lt;br /&gt;
[[File:figure_gaitEvents.png|thumb|center|1000px|caption|&amp;quot;Figure 2: The magnitude of the resultant accelerometer signal along with the detected Heel-Strike and Toe-Off events&amp;quot;]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 120%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;The MATLAB code can be found here&amp;#039;&amp;#039;&amp;#039;: [https://github.com/sidkha99/GaitEventDetection.git Link to Github repository]&amp;lt;/div&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;code&amp;gt;&amp;#039;&amp;#039;&amp;#039;[HS,TO] = SK_gedAlgo(accX,accY,accZ,Fs,winSizeFactor,implement_type);&amp;#039;&amp;#039;&amp;#039;&amp;lt;/code&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;#039;&amp;#039;&amp;#039;Input Arguments:&amp;#039;&amp;#039;&amp;#039;&lt;br /&gt;
&lt;br /&gt;
*accX, accY, accZ [unit: m/s^2]- signals obtained from each individual axis of 3-axes accelerometer&lt;br /&gt;
*Fs [unit: Hz] - sampling frequency of the acceleration signal. Originally developed for 128 Hz&lt;br /&gt;
*winSizeFactor - size of the running window. Default value = 3. Vary this size from 2 to 6 to get better results or if code crashes.&lt;br /&gt;
*implement_type - To detect the gait event, two implementations are provided. Default value: &amp;#039;fit&amp;#039;&lt;br /&gt;
**&amp;#039;fit&amp;#039; - 2D Gaussian distribution fitting is done to estimate gait event (runs slow)&lt;br /&gt;
**&amp;#039;fast&amp;#039;- faster implemention to estimate the event. Warning: &amp;#039;fast&amp;#039; implementation might not give the best estimate of the gait event compared to gaussian fitting. It is not presented in the paper but is solely provided to estimate events faster without gaussian fitting.&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;#039;&amp;#039;&amp;#039;Output Arguments:&amp;#039;&amp;#039;&amp;#039;&lt;br /&gt;
&lt;br /&gt;
*HS - vector containing sample numbers of Heel-Strike occurences&lt;br /&gt;
*TO - vector containing sample numbers of Toe-Off occurences&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 160%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;References&amp;#039;&amp;#039;&amp;#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
----------------------------------------------------&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 120%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;Please remember to include an appropriate citation to acknowledge the use of library in all documents and papers that uses it:&amp;#039;&amp;#039;&amp;#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
----------------------------------------------------&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
[1] Siddhartha Khandelwal; Nicholas Wickström, &amp;quot;Gait Event Detection in Real-World Environment for Long-Term Applications: Incorporating Domain Knowledge into Time-Frequency Analysis,&amp;quot; in IEEE Transactions on Neural Systems and Rehabilitation Engineering , vol.PP, no.99, pp.1-1 &lt;br /&gt;
&lt;br /&gt;
IEEE url: [http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7423805&amp;amp;newsearch=true&amp;amp;queryText=siddhartha%20khandelwal]&lt;br /&gt;
DiVA url: [http://hh.diva-portal.org/smash/record.jsf?searchId=1&amp;amp;pid=diva2:909015]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
[2] Siddhartha Khandelwal; Nicholas Wickström, &amp;quot;Identification of Gait Events using Expert Knowledge and Continuous Wavelet Transform Analysis&amp;quot;, in BIOSIGNALS 2014, 7th International Conference on Bio-inspired Systems and Signal Processing, Angers, France, March 3-6, 2014 &lt;br /&gt;
&lt;br /&gt;
DiVA url: [http://hh.diva-portal.org/smash/record.jsf?searchId=1&amp;amp;pid=diva2:688909]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 120%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;You may also want to read:&amp;#039;&amp;#039;&amp;#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
&lt;br /&gt;
[3] Siddhartha Khandelwal, Nicholas Wickström, Evaluation of the performance of accelerometer-based gait event detection algorithms in different real-world scenarios using the MAREA gait database, Gait &amp;amp; Posture, Volume 51, January 2017, Pages 84-90, ISSN 0966-6362, http://dx.doi.org/10.1016/j.gaitpost.2016.09.023.&lt;br /&gt;
&lt;br /&gt;
Sciencedirect: [http://www.sciencedirect.com/science/article/pii/S0966636216305859]&lt;br /&gt;
Diva: [http://hh.diva-portal.org/smash/record.jsf?searchId=1&amp;amp;pid=diva2%3A999938&amp;amp;dswid=1416]&lt;br /&gt;
&lt;br /&gt;
Link to the MAREA gait database: [http://islab.hh.se/mediawiki/Gait_database MAREA gait databse]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 160%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;Created By&amp;#039;&amp;#039;&amp;#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
----------------------------------------------------&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
[http://islab.hh.se/mediawiki/Siddhartha_Khandelwal Siddhartha Khandelwal]&lt;/div&gt;</summary>
		<author><name>Siddhartha</name></author>
	</entry>
	<entry>
		<id>https://mw.hh.se/caisr/index.php?title=Gait_events&amp;diff=3266</id>
		<title>Gait events</title>
		<link rel="alternate" type="text/html" href="https://mw.hh.se/caisr/index.php?title=Gait_events&amp;diff=3266"/>
		<updated>2016-10-24T16:11:53Z</updated>

		<summary type="html">&lt;p&gt;Siddhartha: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;&lt;br /&gt;
== Gait Event Detection in Real-World Environments for Long-Term Applications ==&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
Detecting gait events is the key to many gait analysis applications that would benefit from continuous monitoring or long-term analysis. Most gait event detection algorithms using wearable sensors that offer a potential for use in daily living have been developed from data collected in controlled indoor experiments. However, for real-word applications, it is essential that the analysis is carried out in humans’ natural environment; that involves different gait speeds, changing walking terrains, varying surface inclinations and regular turns among other factors. Existing domain knowledge in the form of principles or underlying fundamental gait relationships can be utilized to drive and support the data analysis in order to develop robust algorithms that can tackle real-world challenges in gait analysis. This paper presents a novel approach that exhibits how domain knowledge about human gait can be incorporated into time-frequency analysis to detect gait events from longterm accelerometer signals. The accuracy and robustness of the proposed algorithm are validated by experiments done in indoor and outdoor environments with approximately 93,600 gait events in total. The proposed algorithm exhibits consistently high performance scores across all datasets in both, indoor and outdoor environments.&lt;br /&gt;
&lt;br /&gt;
[[File:accPlacement.jpg|right|thumb|200px|caption|&amp;quot;Figure 1: Position and orientation of each accelerometer at the beginning of each experiment. The accelerometer at the right ankle is attached arbitrarily without any pre-defined orientation. The FSRs are instrumented into the shoe soles to collect the ground truth. The data from all sensors is sampled at a frequency of 128 Hz.&amp;quot;]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
==List of Specifications==&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
*&amp;#039;&amp;#039;&amp;#039;Activities&amp;#039;&amp;#039;&amp;#039;: Walking and running&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
*&amp;#039;&amp;#039;&amp;#039;Accelerometer&amp;#039;&amp;#039;&amp;#039;:&lt;br /&gt;
**&amp;#039;&amp;#039;&amp;#039;Placement of 3-axis Accelerometer&amp;#039;&amp;#039;&amp;#039;: Anywhere around the ankle in any orientation as shown in Figure 1. &lt;br /&gt;
**&amp;#039;&amp;#039;&amp;#039;Sensitivity of the Accelerometer&amp;#039;&amp;#039;&amp;#039;: (+-) 4g or more. Please also check if the accelerometer signal has saturated during intense activity such as running. &lt;br /&gt;
**&amp;#039;&amp;#039;&amp;#039;Sampling Frequency&amp;#039;&amp;#039;&amp;#039;: Preferred - 128 Hz [A Sampling frequency of 50Hz and above is acceptable.]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
*&amp;#039;&amp;#039;&amp;#039;Input data&amp;#039;&amp;#039;&amp;#039;: &lt;br /&gt;
**accX - accelerometer data from X - axis&lt;br /&gt;
**accY - accelerometer data from Y - axis&lt;br /&gt;
**accZ - accelerometer data from Z - axis&lt;br /&gt;
**&amp;#039;&amp;#039;&amp;#039;Input data format&amp;#039;&amp;#039;&amp;#039;: The accelerometer signals should be in &amp;#039;&amp;#039;&amp;#039;units of m/s^2&amp;#039;&amp;#039;&amp;#039; and need to be in .mat format [in Matlab file format]. &lt;br /&gt;
**&amp;#039;&amp;#039;&amp;#039;IMPORTANT NOTE: The data should consist of ONLY walking and running segments of the signal. Segments corresponding to inactivity or any other activity should be removed from the signals prior to running the implementation.&amp;#039;&amp;#039;&amp;#039;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
==Implementation==&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
The implementation of the algorithm is provided as a library that can be used to detect gait events from 3-axis accelerometer signals collected during walking or running. &lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 120%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;The MATLAB code can be found here&amp;#039;&amp;#039;&amp;#039;: [https://github.com/sidkha99/GaitEventDetection.git Link to Github repository]&amp;lt;/div&amp;gt;&lt;br /&gt;
&lt;br /&gt;
Link to the MAREA gait database: [http://islab.hh.se/mediawiki/Gait_database MAREA gait databse]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
[[File:figure_gaitEvents.png|thumb|center|1000px|caption|&amp;quot;Figure 2: The magnitude of the resultant accelerometer signal along with the detected Heel-Strike and Toe-Off events&amp;quot;]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 160%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;References&amp;#039;&amp;#039;&amp;#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
----------------------------------------------------&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 120%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;Please remember to include an appropriate citation to acknowledge the use of library in all documents and papers that uses it:&amp;#039;&amp;#039;&amp;#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
----------------------------------------------------&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
[1] Siddhartha Khandelwal; Nicholas Wickström, &amp;quot;Gait Event Detection in Real-World Environment for Long-Term Applications: Incorporating Domain Knowledge into Time-Frequency Analysis,&amp;quot; in IEEE Transactions on Neural Systems and Rehabilitation Engineering , vol.PP, no.99, pp.1-1 &lt;br /&gt;
&lt;br /&gt;
IEEE url: [http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7423805&amp;amp;newsearch=true&amp;amp;queryText=siddhartha%20khandelwal]&lt;br /&gt;
DiVA url: [http://hh.diva-portal.org/smash/record.jsf?searchId=1&amp;amp;pid=diva2:909015]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
[2] Siddhartha Khandelwal; Nicholas Wickström, &amp;quot;Identification of Gait Events using Expert Knowledge and Continuous Wavelet Transform Analysis&amp;quot;, in BIOSIGNALS 2014, 7th International Conference on Bio-inspired Systems and Signal Processing, Angers, France, March 3-6, 2014 &lt;br /&gt;
&lt;br /&gt;
DiVA url: [http://hh.diva-portal.org/smash/record.jsf?searchId=1&amp;amp;pid=diva2:688909]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 120%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;You may also want to read:&amp;#039;&amp;#039;&amp;#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
&lt;br /&gt;
[3] Siddhartha Khandelwal, Nicholas Wickström, Evaluation of the performance of accelerometer-based gait event detection algorithms in different real-world scenarios using the MAREA gait database, Gait &amp;amp; Posture, Volume 51, January 2017, Pages 84-90, ISSN 0966-6362, http://dx.doi.org/10.1016/j.gaitpost.2016.09.023.&lt;br /&gt;
&lt;br /&gt;
Sciencedirect: [http://www.sciencedirect.com/science/article/pii/S0966636216305859]&lt;br /&gt;
Diva: [http://hh.diva-portal.org/smash/record.jsf?searchId=1&amp;amp;pid=diva2%3A999938&amp;amp;dswid=1416]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 160%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;Created By&amp;#039;&amp;#039;&amp;#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
----------------------------------------------------&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
[http://islab.hh.se/mediawiki/Siddhartha_Khandelwal Siddhartha Khandelwal]&lt;/div&gt;</summary>
		<author><name>Siddhartha</name></author>
	</entry>
	<entry>
		<id>https://mw.hh.se/caisr/index.php?title=File:Figure_gaitEvents.png&amp;diff=3265</id>
		<title>File:Figure gaitEvents.png</title>
		<link rel="alternate" type="text/html" href="https://mw.hh.se/caisr/index.php?title=File:Figure_gaitEvents.png&amp;diff=3265"/>
		<updated>2016-10-24T16:06:08Z</updated>

		<summary type="html">&lt;p&gt;Siddhartha: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;&lt;/div&gt;</summary>
		<author><name>Siddhartha</name></author>
	</entry>
	<entry>
		<id>https://mw.hh.se/caisr/index.php?title=Gait_events&amp;diff=3264</id>
		<title>Gait events</title>
		<link rel="alternate" type="text/html" href="https://mw.hh.se/caisr/index.php?title=Gait_events&amp;diff=3264"/>
		<updated>2016-10-24T15:18:09Z</updated>

		<summary type="html">&lt;p&gt;Siddhartha: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;&lt;br /&gt;
== Gait Event Detection in Real-World Environments for Long-Term Applications ==&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
Detecting gait events is the key to many gait analysis applications that would benefit from continuous monitoring or long-term analysis. Most gait event detection algorithms using wearable sensors that offer a potential for use in daily living have been developed from data collected in controlled indoor experiments. However, for real-word applications, it is essential that the analysis is carried out in humans’ natural environment; that involves different gait speeds, changing walking terrains, varying surface inclinations and regular turns among other factors. Existing domain knowledge in the form of principles or underlying fundamental gait relationships can be utilized to drive and support the data analysis in order to develop robust algorithms that can tackle real-world challenges in gait analysis. This paper presents a novel approach that exhibits how domain knowledge about human gait can be incorporated into time-frequency analysis to detect gait events from longterm accelerometer signals. The accuracy and robustness of the proposed algorithm are validated by experiments done in indoor and outdoor environments with approximately 93,600 gait events in total. The proposed algorithm exhibits consistently high performance scores across all datasets in both, indoor and outdoor environments.&lt;br /&gt;
&lt;br /&gt;
[[File:accPlacement.jpg|right|thumb|200px|caption|&amp;quot;Figure 1: Position and orientation of each accelerometer at the beginning of each experiment. The accelerometer at the right ankle is attached arbitrarily without any pre-defined orientation. The FSRs are instrumented into the shoe soles to collect the ground truth. The data from all sensors is sampled at a frequency of 128 Hz.&amp;quot;]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
==List of Specifications==&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
*&amp;#039;&amp;#039;&amp;#039;Activities&amp;#039;&amp;#039;&amp;#039;: Walking and running&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
*&amp;#039;&amp;#039;&amp;#039;Accelerometer&amp;#039;&amp;#039;&amp;#039;:&lt;br /&gt;
**&amp;#039;&amp;#039;&amp;#039;Placement of 3-axis Accelerometer&amp;#039;&amp;#039;&amp;#039;: Anywhere around the ankle in any orientation as shown in Figure 1. &lt;br /&gt;
**&amp;#039;&amp;#039;&amp;#039;Sensitivity of the Accelerometer&amp;#039;&amp;#039;&amp;#039;: (+-) 4g or more. Please also check if the accelerometer signal has saturated during intense activity such as running. &lt;br /&gt;
**&amp;#039;&amp;#039;&amp;#039;Sampling Frequency&amp;#039;&amp;#039;&amp;#039;: Preferred - 128 Hz [A Sampling frequency of 50Hz and above is acceptable.]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
*&amp;#039;&amp;#039;&amp;#039;Input data&amp;#039;&amp;#039;&amp;#039;: &lt;br /&gt;
**accX - accelerometer data from X - axis&lt;br /&gt;
**accY - accelerometer data from Y - axis&lt;br /&gt;
**accZ - accelerometer data from Z - axis&lt;br /&gt;
**&amp;#039;&amp;#039;&amp;#039;Input data format&amp;#039;&amp;#039;&amp;#039;: The accelerometer signals should be in &amp;#039;&amp;#039;&amp;#039;units of m/s^2&amp;#039;&amp;#039;&amp;#039; and need to be in .mat format [in Matlab file format]. &lt;br /&gt;
**&amp;#039;&amp;#039;&amp;#039;IMPORTANT NOTE: The data should consist of ONLY walking and running segments of the signal. Segments corresponding to inactivity or any other activity should be removed from the signals prior to running the implementation.&amp;#039;&amp;#039;&amp;#039;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
==Implementation==&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
The implementation of the algorithm is provided as a library that can be used to detect gait events from 3-axis accelerometer signals collected during walking or running. &lt;br /&gt;
&lt;br /&gt;
&amp;#039;&amp;#039;&amp;#039;The library can be found here&amp;#039;&amp;#039;&amp;#039;: [https://github.com/sidkha99/GaitEventDetection.git Link to Github repository]&lt;br /&gt;
&lt;br /&gt;
Link to the MAREA gait database: [http://islab.hh.se/mediawiki/Gait_database MAREA gait databse]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 160%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;References&amp;#039;&amp;#039;&amp;#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
----------------------------------------------------&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 120%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;Please remember to include an appropriate citation to acknowledge the use of library in all documents and papers that uses it:&amp;#039;&amp;#039;&amp;#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
----------------------------------------------------&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
[1] Siddhartha Khandelwal; Nicholas Wickström, &amp;quot;Gait Event Detection in Real-World Environment for Long-Term Applications: Incorporating Domain Knowledge into Time-Frequency Analysis,&amp;quot; in IEEE Transactions on Neural Systems and Rehabilitation Engineering , vol.PP, no.99, pp.1-1 &lt;br /&gt;
&lt;br /&gt;
IEEE url: [http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7423805&amp;amp;newsearch=true&amp;amp;queryText=siddhartha%20khandelwal]&lt;br /&gt;
DiVA url: [http://hh.diva-portal.org/smash/record.jsf?searchId=1&amp;amp;pid=diva2:909015]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
[2] Siddhartha Khandelwal; Nicholas Wickström, &amp;quot;Identification of Gait Events using Expert Knowledge and Continuous Wavelet Transform Analysis&amp;quot;, in BIOSIGNALS 2014, 7th International Conference on Bio-inspired Systems and Signal Processing, Angers, France, March 3-6, 2014 &lt;br /&gt;
&lt;br /&gt;
DiVA url: [http://hh.diva-portal.org/smash/record.jsf?searchId=1&amp;amp;pid=diva2:688909]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 120%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;You may also want to read:&amp;#039;&amp;#039;&amp;#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
&lt;br /&gt;
[3] Siddhartha Khandelwal, Nicholas Wickström, Evaluation of the performance of accelerometer-based gait event detection algorithms in different real-world scenarios using the MAREA gait database, Gait &amp;amp; Posture, Volume 51, January 2017, Pages 84-90, ISSN 0966-6362, http://dx.doi.org/10.1016/j.gaitpost.2016.09.023.&lt;br /&gt;
&lt;br /&gt;
Sciencedirect: [http://www.sciencedirect.com/science/article/pii/S0966636216305859]&lt;br /&gt;
Diva: [http://hh.diva-portal.org/smash/record.jsf?searchId=1&amp;amp;pid=diva2%3A999938&amp;amp;dswid=1416]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 160%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;Created By&amp;#039;&amp;#039;&amp;#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
----------------------------------------------------&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
[http://islab.hh.se/mediawiki/Siddhartha_Khandelwal Siddhartha Khandelwal]&lt;/div&gt;</summary>
		<author><name>Siddhartha</name></author>
	</entry>
	<entry>
		<id>https://mw.hh.se/caisr/index.php?title=Gait_events&amp;diff=3263</id>
		<title>Gait events</title>
		<link rel="alternate" type="text/html" href="https://mw.hh.se/caisr/index.php?title=Gait_events&amp;diff=3263"/>
		<updated>2016-10-24T15:16:52Z</updated>

		<summary type="html">&lt;p&gt;Siddhartha: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;&lt;br /&gt;
== Gait Event Detection in Real-World Environments for Long-Term Applications ==&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
Detecting gait events is the key to many gait analysis applications that would benefit from continuous monitoring or long-term analysis. Most gait event detection algorithms using wearable sensors that offer a potential for use in daily living have been developed from data collected in controlled indoor experiments. However, for real-word applications, it is essential that the analysis is carried out in humans’ natural environment; that involves different gait speeds, changing walking terrains, varying surface inclinations and regular turns among other factors. Existing domain knowledge in the form of principles or underlying fundamental gait relationships can be utilized to drive and support the data analysis in order to develop robust algorithms that can tackle real-world challenges in gait analysis. This paper presents a novel approach that exhibits how domain knowledge about human gait can be incorporated into time-frequency analysis to detect gait events from longterm accelerometer signals. The accuracy and robustness of the proposed algorithm are validated by experiments done in indoor and outdoor environments with approximately 93,600 gait events in total. The proposed algorithm exhibits consistently high performance scores across all datasets in both, indoor and outdoor environments.&lt;br /&gt;
&lt;br /&gt;
[[File:accPlacement.jpg|right|thumb|200px|caption|&amp;quot;Figure 1: Position and orientation of each accelerometer at the beginning of each experiment. The accelerometer at the right ankle is attached arbitrarily without any pre-defined orientation. The FSRs are instrumented into the shoe soles to collect the ground truth. The data from all sensors is sampled at a frequency of 128 Hz.&amp;quot;]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
==List of Specifications==&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
*&amp;#039;&amp;#039;&amp;#039;Activities&amp;#039;&amp;#039;&amp;#039;: Walking and running&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
*&amp;#039;&amp;#039;&amp;#039;Accelerometer&amp;#039;&amp;#039;&amp;#039;:&lt;br /&gt;
**&amp;#039;&amp;#039;&amp;#039;Placement of 3-axis Accelerometer&amp;#039;&amp;#039;&amp;#039;: Anywhere around the ankle in any orientation as shown in Figure 1. &lt;br /&gt;
**&amp;#039;&amp;#039;&amp;#039;Sensitivity of the Accelerometer&amp;#039;&amp;#039;&amp;#039;: (+-) 4g or more. Please also check if the accelerometer signal has saturated during intense activity such as running. &lt;br /&gt;
**&amp;#039;&amp;#039;&amp;#039;Sampling Frequency&amp;#039;&amp;#039;&amp;#039;: Preferred - 128 Hz [A Sampling frequency of 50Hz and above is acceptable.]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
*&amp;#039;&amp;#039;&amp;#039;Input data&amp;#039;&amp;#039;&amp;#039;: &lt;br /&gt;
**accX - accelerometer data from X - axis&lt;br /&gt;
**accY - accelerometer data from Y - axis&lt;br /&gt;
**accZ - accelerometer data from Z - axis&lt;br /&gt;
**&amp;#039;&amp;#039;&amp;#039;Input data format&amp;#039;&amp;#039;&amp;#039;: The accelerometer signals should be in &amp;#039;&amp;#039;&amp;#039;units of m/s^2&amp;#039;&amp;#039;&amp;#039; and need to be in .mat format [in Matlab file format]. &lt;br /&gt;
**&amp;#039;&amp;#039;&amp;#039;IMPORTANT NOTE: The data should consist of ONLY walking and running segments of the signal. Segments corresponding to inactivity or any other activity should be removed from the signals prior to running the implementation.&amp;#039;&amp;#039;&amp;#039;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
==Implementation==&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
The implementation of the algorithm is provided as a library that can be used to detect gait events from 3-axis accelerometer signals collected during walking or running. &lt;br /&gt;
&lt;br /&gt;
&amp;#039;&amp;#039;&amp;#039;The library can be found here&amp;#039;&amp;#039;&amp;#039;: [https://github.com/sidkha99/GaitEventDetection.git Link to Github repository]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 160%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;References&amp;#039;&amp;#039;&amp;#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
----------------------------------------------------&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 120%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;Please remember to include an appropriate citation to acknowledge the use of library in all documents and papers that uses it:&amp;#039;&amp;#039;&amp;#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
----------------------------------------------------&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
[1] Siddhartha Khandelwal; Nicholas Wickström, &amp;quot;Gait Event Detection in Real-World Environment for Long-Term Applications: Incorporating Domain Knowledge into Time-Frequency Analysis,&amp;quot; in IEEE Transactions on Neural Systems and Rehabilitation Engineering , vol.PP, no.99, pp.1-1 &lt;br /&gt;
&lt;br /&gt;
IEEE url: [http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7423805&amp;amp;newsearch=true&amp;amp;queryText=siddhartha%20khandelwal]&lt;br /&gt;
DiVA url: [http://hh.diva-portal.org/smash/record.jsf?searchId=1&amp;amp;pid=diva2:909015]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
[2] Siddhartha Khandelwal; Nicholas Wickström, &amp;quot;Identification of Gait Events using Expert Knowledge and Continuous Wavelet Transform Analysis&amp;quot;, in BIOSIGNALS 2014, 7th International Conference on Bio-inspired Systems and Signal Processing, Angers, France, March 3-6, 2014 &lt;br /&gt;
&lt;br /&gt;
DiVA url: [http://hh.diva-portal.org/smash/record.jsf?searchId=1&amp;amp;pid=diva2:688909]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 120%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;You may also want to read:&amp;#039;&amp;#039;&amp;#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
&lt;br /&gt;
[3] Siddhartha Khandelwal, Nicholas Wickström, Evaluation of the performance of accelerometer-based gait event detection algorithms in different real-world scenarios using the MAREA gait database, Gait &amp;amp; Posture, Volume 51, January 2017, Pages 84-90, ISSN 0966-6362, http://dx.doi.org/10.1016/j.gaitpost.2016.09.023.&lt;br /&gt;
&lt;br /&gt;
Sciencedirect: [http://www.sciencedirect.com/science/article/pii/S0966636216305859]&lt;br /&gt;
Diva: [http://hh.diva-portal.org/smash/record.jsf?searchId=1&amp;amp;pid=diva2%3A999938&amp;amp;dswid=1416]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 160%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;Created By&amp;#039;&amp;#039;&amp;#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
----------------------------------------------------&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
[http://islab.hh.se/mediawiki/Siddhartha_Khandelwal Siddhartha Khandelwal]&lt;/div&gt;</summary>
		<author><name>Siddhartha</name></author>
	</entry>
	<entry>
		<id>https://mw.hh.se/caisr/index.php?title=Gait_events&amp;diff=3258</id>
		<title>Gait events</title>
		<link rel="alternate" type="text/html" href="https://mw.hh.se/caisr/index.php?title=Gait_events&amp;diff=3258"/>
		<updated>2016-10-20T14:23:37Z</updated>

		<summary type="html">&lt;p&gt;Siddhartha: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;&lt;br /&gt;
== Gait Event Detection in Real-World Environments for Long-Term Applications ==&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
This page provides the library that can be used to detect gait events from 3-axis accelerometer signals collected during walking or running. &lt;br /&gt;
&lt;br /&gt;
The library can be found here: [https://github.com/sidkha99/GaitEventDetection.git Link to Github repository]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
==&amp;#039;&amp;#039;&amp;#039;List of Specifications&amp;#039;&amp;#039;&amp;#039;==&lt;br /&gt;
&lt;br /&gt;
&amp;#039;&amp;#039;&amp;#039;Activities&amp;#039;&amp;#039;&amp;#039;: Walking and running&lt;br /&gt;
&lt;br /&gt;
&amp;#039;&amp;#039;&amp;#039;Placement of 3-axis Accelerometer&amp;#039;&amp;#039;&amp;#039;: Anywhere around the ankle in any orientation.&lt;br /&gt;
&lt;br /&gt;
&amp;#039;&amp;#039;&amp;#039;Sensitivity of the Accelerometer&amp;#039;&amp;#039;&amp;#039;: (+-) 4g or more. Please specify. Please also check if the accelerometer signal has saturated during intense activity such as running. &lt;br /&gt;
&lt;br /&gt;
&amp;#039;&amp;#039;&amp;#039;Sampling Frequency&amp;#039;&amp;#039;&amp;#039;: Preferred - 128 Hz [A Sampling frequency of 50Hz and above is acceptable. Please specify.]&lt;br /&gt;
&lt;br /&gt;
&amp;#039;&amp;#039;&amp;#039;Input data&amp;#039;&amp;#039;&amp;#039;: AccX, AccY, AccZ [Accelerometer signals from the X, Y and Z axes, respectively.]  &lt;br /&gt;
&lt;br /&gt;
&amp;#039;&amp;#039;&amp;#039;Input data format&amp;#039;&amp;#039;&amp;#039;: abc.mat [Data in Matlab format.] &lt;br /&gt;
&lt;br /&gt;
&amp;#039;&amp;#039;&amp;#039;&amp;#039;&amp;#039;NOTE: The data should consist of ONLY walking and running segments of the signal. Segments corresponding to inactivity or any other activity should be removed from the signals prior to data submission.&amp;#039;&amp;#039;&amp;#039;&amp;#039;&amp;#039;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 120%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;Citations&amp;#039;&amp;#039;&amp;#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
----------------------------------------------------&lt;br /&gt;
&lt;br /&gt;
[1] Siddhartha Khandelwal; Nicholas Wickström, &amp;quot;Gait Event Detection in Real-World Environment for Long-Term Applications: Incorporating Domain Knowledge into Time-Frequency Analysis,&amp;quot; in IEEE Transactions on Neural Systems and Rehabilitation Engineering , vol.PP, no.99, pp.1-1 &lt;br /&gt;
&lt;br /&gt;
IEEE url: [http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7423805&amp;amp;newsearch=true&amp;amp;queryText=siddhartha%20khandelwal]&lt;br /&gt;
DiVA url: [http://hh.diva-portal.org/smash/record.jsf?searchId=1&amp;amp;pid=diva2:909015]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
[2] Siddhartha Khandelwal; Nicholas Wickström, &amp;quot;Identification of Gait Events using Expert Knowledge and Continuous Wavelet Transform Analysis&amp;quot;, in BIOSIGNALS 2014, 7th International Conference on Bio-inspired Systems and Signal Processing, Angers, France, March 3-6, 2014 &lt;br /&gt;
&lt;br /&gt;
DiVA url: [http://hh.diva-portal.org/smash/record.jsf?searchId=1&amp;amp;pid=diva2:688909]&lt;/div&gt;</summary>
		<author><name>Siddhartha</name></author>
	</entry>
	<entry>
		<id>https://mw.hh.se/caisr/index.php?title=Gait_events&amp;diff=3257</id>
		<title>Gait events</title>
		<link rel="alternate" type="text/html" href="https://mw.hh.se/caisr/index.php?title=Gait_events&amp;diff=3257"/>
		<updated>2016-10-20T14:22:36Z</updated>

		<summary type="html">&lt;p&gt;Siddhartha: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;&lt;br /&gt;
== Gait Event Detection in Real-World Environments for Long-Term Applications ==&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
This page provides the library that can be used to detect gait events from 3-axis accelerometer signals collected during walking or running. &lt;br /&gt;
&lt;br /&gt;
The library can be found here: [https://github.com/sidkha99/GaitEventDetection.git Link to Github repository]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
==&amp;#039;&amp;#039;&amp;#039;List of Specifications&amp;#039;&amp;#039;&amp;#039;==&lt;br /&gt;
&lt;br /&gt;
&amp;#039;&amp;#039;&amp;#039;Activities&amp;#039;&amp;#039;&amp;#039;: Walking and running&lt;br /&gt;
&lt;br /&gt;
&amp;#039;&amp;#039;&amp;#039;Placement of 3-axis Accelerometer&amp;#039;&amp;#039;&amp;#039;: Anywhere around the ankle in any orientation.&lt;br /&gt;
&lt;br /&gt;
&amp;#039;&amp;#039;&amp;#039;Sensitivity of the Accelerometer&amp;#039;&amp;#039;&amp;#039;: (+-) 4g or more. Please specify. Please also check if the accelerometer signal has saturated during intense activity such as running. &lt;br /&gt;
&lt;br /&gt;
&amp;#039;&amp;#039;&amp;#039;Sampling Frequency&amp;#039;&amp;#039;&amp;#039;: Preferred - 128 Hz [A Sampling frequency of 50Hz and above is acceptable. Please specify.]&lt;br /&gt;
&lt;br /&gt;
&amp;#039;&amp;#039;&amp;#039;Input data&amp;#039;&amp;#039;&amp;#039;: AccX, AccY, AccZ [Accelerometer signals from the X, Y and Z axes, respectively.]  &lt;br /&gt;
&lt;br /&gt;
&amp;#039;&amp;#039;&amp;#039;Input data format&amp;#039;&amp;#039;&amp;#039;: abc.mat or abc.csv [Data in Matlab format or comma separated text file.] &lt;br /&gt;
&lt;br /&gt;
&amp;#039;&amp;#039;&amp;#039;&amp;#039;&amp;#039;NOTE: The data should consist of ONLY walking and running segments of the signal. Segments corresponding to inactivity or any other activity should be removed from the signals prior to data submission.&amp;#039;&amp;#039;&amp;#039;&amp;#039;&amp;#039;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 120%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;Citations&amp;#039;&amp;#039;&amp;#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
----------------------------------------------------&lt;br /&gt;
&lt;br /&gt;
[1] Siddhartha Khandelwal; Nicholas Wickström, &amp;quot;Gait Event Detection in Real-World Environment for Long-Term Applications: Incorporating Domain Knowledge into Time-Frequency Analysis,&amp;quot; in IEEE Transactions on Neural Systems and Rehabilitation Engineering , vol.PP, no.99, pp.1-1 &lt;br /&gt;
&lt;br /&gt;
IEEE url: [http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7423805&amp;amp;newsearch=true&amp;amp;queryText=siddhartha%20khandelwal]&lt;br /&gt;
DiVA url: [http://hh.diva-portal.org/smash/record.jsf?searchId=1&amp;amp;pid=diva2:909015]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
[2] Siddhartha Khandelwal; Nicholas Wickström, &amp;quot;Identification of Gait Events using Expert Knowledge and Continuous Wavelet Transform Analysis&amp;quot;, in BIOSIGNALS 2014, 7th International Conference on Bio-inspired Systems and Signal Processing, Angers, France, March 3-6, 2014 &lt;br /&gt;
&lt;br /&gt;
DiVA url: [http://hh.diva-portal.org/smash/record.jsf?searchId=1&amp;amp;pid=diva2:688909]&lt;/div&gt;</summary>
		<author><name>Siddhartha</name></author>
	</entry>
	<entry>
		<id>https://mw.hh.se/caisr/index.php?title=Gait_events&amp;diff=3256</id>
		<title>Gait events</title>
		<link rel="alternate" type="text/html" href="https://mw.hh.se/caisr/index.php?title=Gait_events&amp;diff=3256"/>
		<updated>2016-10-20T14:03:00Z</updated>

		<summary type="html">&lt;p&gt;Siddhartha: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;&lt;br /&gt;
== Gait Event Detection in Real-World Environments for Long-Term Applications ==&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
This page provides the library that can be used to detect gait events from 3-axis accelerometer signals collected during walking or running. The implemented algorithm is described in &amp;#039;&amp;#039;Siddhartha Khandelwal; Nicholas Wickström, &amp;quot;Gait Event Detection in Real-World Environment for Long-Term Applications: Incorporating Domain Knowledge into Time-Frequency Analysis,&amp;quot; in IEEE Transactions on Neural Systems and Rehabilitation Engineering , vol.PP, no.99, pp.1-1 &amp;#039;&amp;#039;.&lt;br /&gt;
&lt;br /&gt;
The library can be found here: [https://github.com/sidkha99/GaitEventDetection.git Link to Github repository]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
==&amp;#039;&amp;#039;&amp;#039;List of Specifications&amp;#039;&amp;#039;&amp;#039;==&lt;br /&gt;
&lt;br /&gt;
&amp;#039;&amp;#039;&amp;#039;Activities&amp;#039;&amp;#039;&amp;#039;: Walking and running&lt;br /&gt;
&lt;br /&gt;
&amp;#039;&amp;#039;&amp;#039;Placement of 3-axis Accelerometer&amp;#039;&amp;#039;&amp;#039;: Anywhere around the ankle in any orientation.&lt;br /&gt;
&lt;br /&gt;
&amp;#039;&amp;#039;&amp;#039;Sensitivity of the Accelerometer&amp;#039;&amp;#039;&amp;#039;: (+-) 4g or more. Please specify. Please also check if the accelerometer signal has saturated during intense activity such as running. &lt;br /&gt;
&lt;br /&gt;
&amp;#039;&amp;#039;&amp;#039;Sampling Frequency&amp;#039;&amp;#039;&amp;#039;: Preferred - 128 Hz [A Sampling frequency of 50Hz and above is acceptable. Please specify.]&lt;br /&gt;
&lt;br /&gt;
&amp;#039;&amp;#039;&amp;#039;Input data&amp;#039;&amp;#039;&amp;#039;: AccX, AccY, AccZ [Accelerometer signals from the X, Y and Z axes, respectively.]  &lt;br /&gt;
&lt;br /&gt;
&amp;#039;&amp;#039;&amp;#039;Input data format&amp;#039;&amp;#039;&amp;#039;: abc.mat or abc.csv [Data in Matlab format or comma separated text file.] &lt;br /&gt;
&lt;br /&gt;
&amp;#039;&amp;#039;&amp;#039;&amp;#039;&amp;#039;NOTE: The data should consist of ONLY walking and running segments of the signal. Segments corresponding to inactivity or any other activity should be removed from the signals prior to data submission.&amp;#039;&amp;#039;&amp;#039;&amp;#039;&amp;#039;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 120%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;Citations&amp;#039;&amp;#039;&amp;#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
----------------------------------------------------&lt;br /&gt;
&lt;br /&gt;
[1] Siddhartha Khandelwal; Nicholas Wickström, &amp;quot;Gait Event Detection in Real-World Environment for Long-Term Applications: Incorporating Domain Knowledge into Time-Frequency Analysis,&amp;quot; in IEEE Transactions on Neural Systems and Rehabilitation Engineering , vol.PP, no.99, pp.1-1 &lt;br /&gt;
&lt;br /&gt;
IEEE url: [http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7423805&amp;amp;newsearch=true&amp;amp;queryText=siddhartha%20khandelwal]&lt;br /&gt;
DiVA url: [http://hh.diva-portal.org/smash/record.jsf?searchId=1&amp;amp;pid=diva2:909015]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
[2] Siddhartha Khandelwal; Nicholas Wickström, &amp;quot;Identification of Gait Events using Expert Knowledge and Continuous Wavelet Transform Analysis&amp;quot;, in BIOSIGNALS 2014, 7th International Conference on Bio-inspired Systems and Signal Processing, Angers, France, March 3-6, 2014 &lt;br /&gt;
&lt;br /&gt;
DiVA url: [http://hh.diva-portal.org/smash/record.jsf?searchId=1&amp;amp;pid=diva2:688909]&lt;/div&gt;</summary>
		<author><name>Siddhartha</name></author>
	</entry>
	<entry>
		<id>https://mw.hh.se/caisr/index.php?title=Gait_database:_Release_agreement&amp;diff=3240</id>
		<title>Gait database: Release agreement</title>
		<link rel="alternate" type="text/html" href="https://mw.hh.se/caisr/index.php?title=Gait_database:_Release_agreement&amp;diff=3240"/>
		<updated>2016-10-11T11:21:07Z</updated>

		<summary type="html">&lt;p&gt;Siddhartha: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== Introduction ==&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
The MAREA Gait Database is meant to further research in gait analysis and related fields.  &lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== Release of the Database == &lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
This database can be downloaded as a zip file with password protection which shall be issued on a case-by-case basis. To receive the password, the requester must read and agree to the terms of usage of the database and fill the registration form. The form shall be evaluated and a link to download the database along with the password will be sent to the registered email address. Failure to observe this procedure may result in access being denied for the database.&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== Terms of Usage == &lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
The researcher(s) agrees to the following terms and conditions on the MAREA Gait Database:&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
1. &amp;#039;&amp;#039;&amp;#039;Redistribution&amp;#039;&amp;#039;&amp;#039;: The database, in whole or in part, will not be further distributed, published, copied, or disseminated in any way or form whatsoever, whether for profit or not. This&lt;br /&gt;
includes further distributing, copying or disseminating to a different facility or organizational unit in the requesting university, organization, or company.&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
2. &amp;#039;&amp;#039;&amp;#039;Modification and Commercial Use&amp;#039;&amp;#039;&amp;#039;: The database, in whole or in part, may not be modified or used for commercial purposes.&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
3. &amp;#039;&amp;#039;&amp;#039;Authorization&amp;#039;&amp;#039;&amp;#039;: All rights in and relating to the database remain with Halmstad University.&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
4. &amp;#039;&amp;#039;&amp;#039;Citation/Reference&amp;#039;&amp;#039;&amp;#039;: All documents and papers that report on research that uses the MAREA Gait Database will acknowledge&lt;br /&gt;
the use of the database by including an appropriate citation to the following:&lt;br /&gt;
	&lt;br /&gt;
&amp;#039;&amp;#039;Siddhartha Khandelwal, Nicholas Wickström, Evaluation of the performance of accelerometer-based gait event detection algorithms in different real-world scenarios using the MAREA gait database, Gait &amp;amp; Posture, Volume 51, January 2017, Pages 84-90, ISSN 0966-6362, http://dx.doi.org/10.1016/j.gaitpost.2016.09.023.&amp;#039;&amp;#039;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
6. &amp;#039;&amp;#039;&amp;#039;Publications&amp;#039;&amp;#039;&amp;#039;: A copy of all reports and papers that are for public or general release that use the database will be forwarded prior to or after release to Siddhartha Khandelwal (siddhartha.khandelwal@hh.se) and Nicholas Wickström (nicholas@hh.se).&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
7. &amp;#039;&amp;#039;&amp;#039;Indemnification&amp;#039;&amp;#039;&amp;#039;: Researcher agrees to indemnify, defend, and hold harmless Halmstad University, individually and collectively, from any and all losses, expenses, damages, demands and/or claims based upon any such injury or damage (real or alleged) and shall pay all damages, claims, judgments or expenses resulting from researcher’s use of the database.&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== Registration Form ==&lt;br /&gt;
&lt;br /&gt;
Link to fill the registration form: [https://goo.gl/forms/AFJhaP3qyUzBBnk63 click_registration form ]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
----&lt;/div&gt;</summary>
		<author><name>Siddhartha</name></author>
	</entry>
	<entry>
		<id>https://mw.hh.se/caisr/index.php?title=Gait_database&amp;diff=3239</id>
		<title>Gait database</title>
		<link rel="alternate" type="text/html" href="https://mw.hh.se/caisr/index.php?title=Gait_database&amp;diff=3239"/>
		<updated>2016-10-11T11:20:24Z</updated>

		<summary type="html">&lt;p&gt;Siddhartha: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== MAREA (&amp;#039;&amp;#039;&amp;#039;M&amp;#039;&amp;#039;&amp;#039;ovement &amp;#039;&amp;#039;&amp;#039;A&amp;#039;&amp;#039;&amp;#039;nalysis in &amp;#039;&amp;#039;&amp;#039;R&amp;#039;&amp;#039;&amp;#039;eal-world &amp;#039;&amp;#039;&amp;#039;E&amp;#039;&amp;#039;&amp;#039;nvironments using &amp;#039;&amp;#039;&amp;#039;A&amp;#039;&amp;#039;&amp;#039;ccelerometers) Gait Database ==&lt;br /&gt;
&lt;br /&gt;
[[File:accPlacement.jpg|right|thumb|200px|caption|&amp;quot;Figure 1: Position and orientation of each accelerometer at the beginning of each experiment. The accelerometer at the right ankle is attached arbitrarily without any pre-defined orientation. The FSRs are instrumented into the shoe soles to collect the ground truth. The data from all sensors is sampled at a frequency of 128 Hz.&amp;quot;]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
The &amp;#039;&amp;#039;&amp;#039;MAREA&amp;#039;&amp;#039;&amp;#039; gait database comprises of gait activities in different real-world environments as shown in the table below. 20 healthy adults (12 males and 8 females, average age: 33.4 +- 7 years, average mass: 73.2 +- 10.9 kg, average height: 172.6 +- 9.5 cm) participated in the study that was approved by the Ethical Review Board of Lund, Sweden. Each subject had a 3-axes Shimmer3 (Shimmer Research, Dublin, Ireland) accelerometer (+- 8g) attached to their waist, left wrist and left and right ankles using elastic bands and velcro straps. Figure 1 shows the position and orientation of each accelerometer at the beginning of each experiment. On the waist, the accelerometer X and Y axes were pointing to the lateral and downward direction, respectively. On the wrist and left ankle, the Z axis was pointing in the lateral direction while the Y axis was pointing downward and was aligned with the limb longitudinal axis. In order to simulate a lesser controlled scenario, the accelerometer on right ankle was positioned such that the Y axis was pointing downward but the Z axis was marginally disturbed such that it was not exactly perpendicular to the sagittal plane. The subjects were provided shoes that were instrumented with piezo-electric force sensitive resistors (FSRs), fixed at the extreme ends of the sole in order to provide the ground truth values for HS and TO. An external expansion board was used to synchronously collect the data from the FSRs on each foot and the respective ankle accelerometer, at a sampling frequency of 128Hz, and stored locally on the Shimmer3 microSD card. Due to the lack of a centralized data logger, the waist accelerometer was not in perfect synchronization with the ankle accelerometer.&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
{| class=&amp;quot;wikitable&amp;quot;&lt;br /&gt;
! style=&amp;quot;text-align: center;&amp;quot; | Subjects&lt;br /&gt;
! style=&amp;quot;text-align: center;&amp;quot; | Environment&lt;br /&gt;
! style=&amp;quot;text-align: center;&amp;quot; | Activity&lt;br /&gt;
! style=&amp;quot;text-align: center;&amp;quot; | Speed&lt;br /&gt;
! style=&amp;quot;text-align: center;&amp;quot; | Duration&lt;br /&gt;
! style=&amp;quot;text-align: center;&amp;quot; | Short Description&lt;br /&gt;
|-&lt;br /&gt;
| rowspan=&amp;quot;4&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | 11&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | Treadmill (flat)&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | Walk &amp;amp; run&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | 4km/hr - 8km/hr; increasing in steps&lt;br /&gt;
 of 0.4km/hr every minute&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | 10 min&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | Start walking and switch to &lt;br /&gt;
running at self-selected speed&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | Treadmill (slope)&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | Walk&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | Self-selected&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | 12 min&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | Treadmill is set to (5, 0, 10, 0, 15, 0) degree&lt;br /&gt;
inclinations with 2 mins at each angle&lt;br /&gt;
|-&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Indoor flat space&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Walk &amp;amp; run&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Self-selected&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | 6 min&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Start walking and switch &lt;br /&gt;
to running after 3 mins&lt;br /&gt;
|-&lt;br /&gt;
 |&lt;br /&gt;
|-&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | 9&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Outdoor street&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Walk &amp;amp; run&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Self-selected&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | 6 min&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Start walking and switch &lt;br /&gt;
to running after 3 mins&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
|}&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
==&amp;#039;&amp;#039;&amp;#039;Explanation of the database files&amp;#039;&amp;#039;&amp;#039; (download link below)==&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
=== 1. Activity Timings === &lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
There are two matrices provided, namely, &amp;#039;&amp;#039;&amp;#039;Indoor Experiment timings&amp;#039;&amp;#039;&amp;#039; and  &amp;#039;&amp;#039;&amp;#039;Outdoor Experiment timings&amp;#039;&amp;#039;&amp;#039; which contain the timing information of the activities and are included in a folder called Activity Timings.zip: &lt;br /&gt;
&lt;br /&gt;
[[File:ActivityTimingsGraph.png|right|thumb|500px|caption|&amp;quot;Example of how to use the sample numbers given in Indoor Experiment Timings matrix to extract desired activity data. This figure shows the entire Indoor data collected from accelerometer positioned at Right Ankle for Subject 5. &amp;quot;]]&lt;br /&gt;
&lt;br /&gt;
* &amp;#039;&amp;#039;&amp;#039;Indoor Experiment Timings&amp;#039;&amp;#039;&amp;#039;: This 11(rows) x 8(columns) matrix consists of &amp;#039;&amp;#039;&amp;#039;sample numbers&amp;#039;&amp;#039;&amp;#039; corresponding to the start and end of an activity, for a given subject, for the &amp;#039;&amp;#039;&amp;#039;Indoor Experiments&amp;#039;&amp;#039;&amp;#039;, i.e. &lt;br /&gt;
**Treadmill (flat)&lt;br /&gt;
**Treadmill (slope) &lt;br /&gt;
**Indoor flat space. &lt;br /&gt;
The figure below explains how to extract the sample nos. corresponding to an activity for a given subject, from the Indoor Experiment timings matrix.  &lt;br /&gt;
&lt;br /&gt;
[[File:Indoor Table.png|700px]]&lt;br /&gt;
&lt;br /&gt;
* &amp;#039;&amp;#039;&amp;#039;Outdoor Experiment Timings&amp;#039;&amp;#039;&amp;#039;: This 9(rows) x 3(columns) matrix consists of &amp;#039;&amp;#039;&amp;#039;sample numbers&amp;#039;&amp;#039;&amp;#039; corresponding to the start and end of an activity, for a given subject, for the &amp;#039;&amp;#039;&amp;#039;Outdoor Street Experiments&amp;#039;&amp;#039;&amp;#039;. The figure below explains how to extract the sample nos. corresponding to an activity for a given subject, from the Outdoor Experiment timings matrix.&lt;br /&gt;
&lt;br /&gt;
[[File:Outdoor Table.png|800px]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
=== 2. Subject Data === &lt;br /&gt;
&lt;br /&gt;
[[File:AccXYZ.png|right|thumb|400px|caption|&amp;quot;Figure 2: Example of the accelerometer data from the X, Y and Z axis for Sub5_RF&amp;quot;]]&lt;br /&gt;
&lt;br /&gt;
The Subject data files are provided in two formats, namely, .mat format (&amp;#039;&amp;#039;&amp;#039;Subject Data_mat format.zip&amp;#039;&amp;#039;&amp;#039;) and .txt format &amp;#039;&amp;#039;&amp;#039;Subject Data_txt format).zip&amp;#039;&amp;#039;&amp;#039;. The naming convention of the files is: Sub&amp;lt;number&amp;gt;_&amp;lt;position&amp;gt;, where:&lt;br /&gt;
*&amp;lt;number&amp;gt;: stands for Subject number and ranges from 1 to 20. &lt;br /&gt;
*&amp;lt;position&amp;gt;: stands for the position of the accelerometer on the body as shown in Figure 1. The positions are:&lt;br /&gt;
**LF - Left Ankle&lt;br /&gt;
**RF - Right Ankle&lt;br /&gt;
**Wrist&lt;br /&gt;
**Waist&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
For each Subject file, eg. Sub5_RF, the accelerometer data from the 3 axis accelerometer is stored in 3 columns (separated using comma in the .txt files), each named as: &lt;br /&gt;
*accX - data from X - axis&lt;br /&gt;
*accY - data from Y - axis&lt;br /&gt;
*accZ - data from Z - axis&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
===3. Ground Truth===&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
The Ground Truth is provided as a 11x7 structure that gives the timing of the Heel-Strike and Toe-Off events extracted from the FSR signals. This timing information is provided in terms of sample numbers and is relative to each activity. The structure consists of 7 fields that have &amp;#039;&amp;#039;&amp;#039;already been segregated&amp;#039;&amp;#039;&amp;#039; using the Indoor Experiment Timings and Outdoor Experiment Timings explained above: &lt;br /&gt;
*treadWalk       - treadmill (flat) walk &lt;br /&gt;
&lt;br /&gt;
*treadIncline    - treadmill (slope) walk&lt;br /&gt;
&lt;br /&gt;
*treadWalknRun   - treadmill (flat) walk &amp;amp; run&lt;br /&gt;
&lt;br /&gt;
*indoorWalk      - indoor (flat space) walk&lt;br /&gt;
&lt;br /&gt;
*indoorWalknRun  - indoor (flat space) walk &amp;amp; run&lt;br /&gt;
&lt;br /&gt;
*outdoorWalk     - outdoor street walk&lt;br /&gt;
&lt;br /&gt;
*outdoorWalknRun - outdoor street walk &amp;amp; run &lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
Each row of the structure represents a Subject. For Indoor activities, Row 1 -&amp;gt; Subject1, ..., Row 11 -&amp;gt; Subject 11 and so on. For outdoor activities, Row 1 -&amp;gt; Subject 12,..., Row 9 -&amp;gt; Subject 20. As an example: GroundTruth(1).treadWalk provides the ground truth data for Subject 1 for treadmill (flat) walk. This is a structure with fields: &lt;br /&gt;
* SubIdx - Subject number. Ranges from Sub1 - Sub20&lt;br /&gt;
&lt;br /&gt;
* LF_HS - Sample numbers of Heel-Strike event from Left Foot FSR signal.&lt;br /&gt;
&lt;br /&gt;
* LF_TO - Sample numbers of Toe-Off event from Left Foot FSR signal.&lt;br /&gt;
&lt;br /&gt;
* RF_HS - Sample numbers of Heel-Strike event  from Right Foot FSR signal. &lt;br /&gt;
&lt;br /&gt;
* RF_TO - Sample numbers of Toe-Off event from Right Foot FSR signal.&lt;br /&gt;
&lt;br /&gt;
[[File:GroundTruthStructure.png|1200px]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
=== 4. mainScript.m === &lt;br /&gt;
&lt;br /&gt;
This MATLAB script is an example on how to:&lt;br /&gt;
&lt;br /&gt;
* Read the accelerometer data of a subject for an activity &amp;amp; position&lt;br /&gt;
&lt;br /&gt;
* Read the Ground Truth Heel-Strike and Toe-Off gait events for the same&lt;br /&gt;
&lt;br /&gt;
* Plot all the signals&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
----&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 160%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;How to get the datasets?&amp;#039;&amp;#039;&amp;#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
&lt;br /&gt;
----------------------------------------------------------------------------------------------------------------------------------&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 110%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;1. Link to the Release Agreement and fill Registration Form:&amp;#039;&amp;#039;&amp;#039;  [http://islab.hh.se/mediawiki/Gait_database:_Release_agreement Click_Release Agreement]&amp;lt;/div&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 110%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;2. Link to download the datasets:&amp;#039;&amp;#039;&amp;#039;  Will be sent by Email after Registration Procedure is completed &amp;lt;/div&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 160%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;References&amp;#039;&amp;#039;&amp;#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
----------------------------------------------------&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 120%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;Please remember to include an appropriate citation to acknowledge the use of the database in all documents and papers that uses the MAREA Gait Database:&amp;#039;&amp;#039;&amp;#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
1. Siddhartha Khandelwal, Nicholas Wickström, Evaluation of the performance of accelerometer-based gait event detection algorithms in different real-world scenarios using the MAREA gait database, Gait &amp;amp; Posture, Volume 51, January 2017, Pages 84-90, ISSN 0966-6362, http://dx.doi.org/10.1016/j.gaitpost.2016.09.023.&lt;br /&gt;
&lt;br /&gt;
Sciencedirect: [http://www.sciencedirect.com/science/article/pii/S0966636216305859]&lt;br /&gt;
Diva: [http://hh.diva-portal.org/smash/record.jsf?searchId=1&amp;amp;pid=diva2%3A999938&amp;amp;dswid=1416]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
2. Siddhartha Khandelwal; Nicholas Wickström, &amp;quot;Gait Event Detection in Real-World Environment for Long-Term Applications: Incorporating Domain Knowledge into Time-Frequency Analysis,&amp;quot; in IEEE Transactions on Neural Systems and Rehabilitation Engineering , vol.PP, no.99, pp.1-1, 2016 &lt;br /&gt;
&lt;br /&gt;
IEEE url: [http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7423805&amp;amp;newsearch=true&amp;amp;queryText=siddhartha%20khandelwal]&lt;br /&gt;
DiVA url: [http://hh.diva-portal.org/smash/record.jsf?searchId=1&amp;amp;pid=diva2:909015]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 120%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;You may also want to read:&amp;#039;&amp;#039;&amp;#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
3. Siddhartha Khandelwal; Nicholas Wickström, &amp;quot;Identification of Gait Events using Expert Knowledge and Continuous Wavelet Transform Analysis&amp;quot;, in BIOSIGNALS 2014, 7th International Conference on Bio-inspired Systems and Signal Processing, Angers, France, March 3-6, 2014 &lt;br /&gt;
&lt;br /&gt;
DiVA url: [http://hh.diva-portal.org/smash/record.jsf?searchId=1&amp;amp;pid=diva2:688909]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
4. Siddhartha Khandelwal, Nicholas Wickström, &amp;quot;Detecting Gait Events from Outdoor Accelerometer Data for Long-term and Continuous Monitoring Applications&amp;quot; in 13th International Symposium on 3D Analysis of Human Movement (3D-AHM 2014), 14–17 July, 2014, Lausanne, Switzerland.&lt;br /&gt;
&lt;br /&gt;
DiVA url: [http://www.diva-portal.org/smash/record.jsf?pid=diva2:735193]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 160%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;Created By&amp;#039;&amp;#039;&amp;#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
----------------------------------------------------&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
[http://islab.hh.se/mediawiki/Siddhartha_Khandelwal Siddhartha Khandelwal]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
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&lt;br /&gt;
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&lt;br /&gt;
----&lt;/div&gt;</summary>
		<author><name>Siddhartha</name></author>
	</entry>
	<entry>
		<id>https://mw.hh.se/caisr/index.php?title=Gait_database&amp;diff=3238</id>
		<title>Gait database</title>
		<link rel="alternate" type="text/html" href="https://mw.hh.se/caisr/index.php?title=Gait_database&amp;diff=3238"/>
		<updated>2016-10-10T15:16:29Z</updated>

		<summary type="html">&lt;p&gt;Siddhartha: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== MAREA (&amp;#039;&amp;#039;&amp;#039;M&amp;#039;&amp;#039;&amp;#039;ovement &amp;#039;&amp;#039;&amp;#039;A&amp;#039;&amp;#039;&amp;#039;nalysis in &amp;#039;&amp;#039;&amp;#039;R&amp;#039;&amp;#039;&amp;#039;eal-world &amp;#039;&amp;#039;&amp;#039;E&amp;#039;&amp;#039;&amp;#039;nvironments using &amp;#039;&amp;#039;&amp;#039;A&amp;#039;&amp;#039;&amp;#039;ccelerometers) Gait Database ==&lt;br /&gt;
&lt;br /&gt;
[[File:accPlacement.jpg|right|thumb|200px|caption|&amp;quot;Figure 1: Position and orientation of each accelerometer at the beginning of each experiment. The accelerometer at the right ankle is attached arbitrarily without any pre-defined orientation. The FSRs are instrumented into the shoe soles to collect the ground truth. The data from all sensors is sampled at a frequency of 128 Hz.&amp;quot;]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
The &amp;#039;&amp;#039;&amp;#039;MAREA&amp;#039;&amp;#039;&amp;#039; gait database comprises of gait activities in different real-world environments as shown in the table below. 20 healthy adults (12 males and 8 females, average age: 33.4 +- 7 years, average mass: 73.2 +- 10.9 kg, average height: 172.6 +- 9.5 cm) participated in the study that was approved by the Ethical Review Board of Lund, Sweden. Each subject had a 3-axes Shimmer3 (Shimmer Research, Dublin, Ireland) accelerometer (+- 8g) attached to their waist, left wrist and left and right ankles using elastic bands and velcro straps. Figure 1 shows the position and orientation of each accelerometer at the beginning of each experiment. On the waist, the accelerometer X and Y axes were pointing to the lateral and downward direction, respectively. On the wrist and left ankle, the Z axis was pointing in the lateral direction while the Y axis was pointing downward and was aligned with the limb longitudinal axis. In order to simulate a lesser controlled scenario, the accelerometer on right ankle was positioned such that the Y axis was pointing downward but the Z axis was marginally disturbed such that it was not exactly perpendicular to the sagittal plane. The subjects were provided shoes that were instrumented with piezo-electric force sensitive resistors (FSRs), fixed at the extreme ends of the sole in order to provide the ground truth values for HS and TO. An external expansion board was used to synchronously collect the data from the FSRs on each foot and the respective ankle accelerometer, at a sampling frequency of 128Hz, and stored locally on the Shimmer3 microSD card. Due to the lack of a centralized data logger, the waist accelerometer was not in perfect synchronization with the ankle accelerometer.&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
{| class=&amp;quot;wikitable&amp;quot;&lt;br /&gt;
! style=&amp;quot;text-align: center;&amp;quot; | Subjects&lt;br /&gt;
! style=&amp;quot;text-align: center;&amp;quot; | Environment&lt;br /&gt;
! style=&amp;quot;text-align: center;&amp;quot; | Activity&lt;br /&gt;
! style=&amp;quot;text-align: center;&amp;quot; | Speed&lt;br /&gt;
! style=&amp;quot;text-align: center;&amp;quot; | Duration&lt;br /&gt;
! style=&amp;quot;text-align: center;&amp;quot; | Short Description&lt;br /&gt;
|-&lt;br /&gt;
| rowspan=&amp;quot;4&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | 11&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | Treadmill (flat)&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | Walk &amp;amp; run&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | 4km/hr - 8km/hr; increasing in steps&lt;br /&gt;
 of 0.4km/hr every minute&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | 10 min&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | Start walking and switch to &lt;br /&gt;
running at self-selected speed&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | Treadmill (slope)&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | Walk&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | Self-selected&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | 12 min&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | Treadmill is set to (5, 0, 10, 0, 15, 0) degree&lt;br /&gt;
inclinations with 2 mins at each angle&lt;br /&gt;
|-&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Indoor flat space&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Walk &amp;amp; run&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Self-selected&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | 6 min&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Start walking and switch &lt;br /&gt;
to running after 3 mins&lt;br /&gt;
|-&lt;br /&gt;
 |&lt;br /&gt;
|-&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | 9&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Outdoor street&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Walk &amp;amp; run&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Self-selected&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | 6 min&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Start walking and switch &lt;br /&gt;
to running after 3 mins&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
|}&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
==&amp;#039;&amp;#039;&amp;#039;Explanation of the database files&amp;#039;&amp;#039;&amp;#039; (download link below)==&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
=== 1. Activity Timings === &lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
There are two matrices provided, namely, &amp;#039;&amp;#039;&amp;#039;Indoor Experiment timings&amp;#039;&amp;#039;&amp;#039; and  &amp;#039;&amp;#039;&amp;#039;Outdoor Experiment timings&amp;#039;&amp;#039;&amp;#039; which contain the timing information of the activities and are included in a folder called Activity Timings.zip: &lt;br /&gt;
&lt;br /&gt;
[[File:ActivityTimingsGraph.png|right|thumb|500px|caption|&amp;quot;Example of how to use the sample numbers given in Indoor Experiment Timings matrix to extract desired activity data. This figure shows the entire Indoor data collected from accelerometer positioned at Right Ankle for Subject 5. &amp;quot;]]&lt;br /&gt;
&lt;br /&gt;
* &amp;#039;&amp;#039;&amp;#039;Indoor Experiment Timings&amp;#039;&amp;#039;&amp;#039;: This 11(rows) x 8(columns) matrix consists of &amp;#039;&amp;#039;&amp;#039;sample numbers&amp;#039;&amp;#039;&amp;#039; corresponding to the start and end of an activity, for a given subject, for the &amp;#039;&amp;#039;&amp;#039;Indoor Experiments&amp;#039;&amp;#039;&amp;#039;, i.e. &lt;br /&gt;
**Treadmill (flat)&lt;br /&gt;
**Treadmill (slope) &lt;br /&gt;
**Indoor flat space. &lt;br /&gt;
The figure below explains how to extract the sample nos. corresponding to an activity for a given subject, from the Indoor Experiment timings matrix.  &lt;br /&gt;
&lt;br /&gt;
[[File:Indoor Table.png|700px]]&lt;br /&gt;
&lt;br /&gt;
* &amp;#039;&amp;#039;&amp;#039;Outdoor Experiment Timings&amp;#039;&amp;#039;&amp;#039;: This 9(rows) x 3(columns) matrix consists of &amp;#039;&amp;#039;&amp;#039;sample numbers&amp;#039;&amp;#039;&amp;#039; corresponding to the start and end of an activity, for a given subject, for the &amp;#039;&amp;#039;&amp;#039;Outdoor Street Experiments&amp;#039;&amp;#039;&amp;#039;. The figure below explains how to extract the sample nos. corresponding to an activity for a given subject, from the Outdoor Experiment timings matrix.&lt;br /&gt;
&lt;br /&gt;
[[File:Outdoor Table.png|800px]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
=== 2. Subject Data === &lt;br /&gt;
&lt;br /&gt;
[[File:AccXYZ.png|right|thumb|400px|caption|&amp;quot;Figure 2: Example of the accelerometer data from the X, Y and Z axis for Sub5_RF&amp;quot;]]&lt;br /&gt;
&lt;br /&gt;
The Subject data files are provided in two formats, namely, .mat format (&amp;#039;&amp;#039;&amp;#039;Subject Data_mat format.zip&amp;#039;&amp;#039;&amp;#039;) and .txt format &amp;#039;&amp;#039;&amp;#039;Subject Data_txt format).zip&amp;#039;&amp;#039;&amp;#039;. The naming convention of the files is: Sub&amp;lt;number&amp;gt;_&amp;lt;position&amp;gt;, where:&lt;br /&gt;
*&amp;lt;number&amp;gt;: stands for Subject number and ranges from 1 to 20. &lt;br /&gt;
*&amp;lt;position&amp;gt;: stands for the position of the accelerometer on the body as shown in Figure 1. The positions are:&lt;br /&gt;
**LF - Left Ankle&lt;br /&gt;
**RF - Right Ankle&lt;br /&gt;
**Wrist&lt;br /&gt;
**Waist&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
For each Subject file, eg. Sub5_RF, the accelerometer data from the 3 axis accelerometer is stored in 3 columns (separated using comma in the .txt files), each named as: &lt;br /&gt;
*accX - data from X - axis&lt;br /&gt;
*accY - data from Y - axis&lt;br /&gt;
*accZ - data from Z - axis&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
===3. Ground Truth===&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
The Ground Truth is provided as a 11x7 structure that gives the timing of the Heel-Strike and Toe-Off events extracted from the FSR signals. This timing information is provided in terms of sample numbers and is relative to each activity. The structure consists of 7 fields that have &amp;#039;&amp;#039;&amp;#039;already been segregated&amp;#039;&amp;#039;&amp;#039; using the Indoor Experiment Timings and Outdoor Experiment Timings explained above: &lt;br /&gt;
*treadWalk       - treadmill (flat) walk &lt;br /&gt;
&lt;br /&gt;
*treadIncline    - treadmill (slope) walk&lt;br /&gt;
&lt;br /&gt;
*treadWalknRun   - treadmill (flat) walk &amp;amp; run&lt;br /&gt;
&lt;br /&gt;
*indoorWalk      - indoor (flat space) walk&lt;br /&gt;
&lt;br /&gt;
*indoorWalknRun  - indoor (flat space) walk &amp;amp; run&lt;br /&gt;
&lt;br /&gt;
*outdoorWalk     - outdoor street walk&lt;br /&gt;
&lt;br /&gt;
*outdoorWalknRun - outdoor street walk &amp;amp; run &lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
Each row of the structure represents a Subject. For Indoor activities, Row 1 -&amp;gt; Subject1, ..., Row 11 -&amp;gt; Subject 11 and so on. For outdoor activities, Row 1 -&amp;gt; Subject 12,..., Row 9 -&amp;gt; Subject 20. As an example: GroundTruth(1).treadWalk provides the ground truth data for Subject 1 for treadmill (flat) walk. This is a structure with fields: &lt;br /&gt;
* SubIdx - Subject number. Ranges from Sub1 - Sub20&lt;br /&gt;
&lt;br /&gt;
* LF_HS - Sample numbers of Heel-Strike event from Left Foot FSR signal.&lt;br /&gt;
&lt;br /&gt;
* LF_TO - Sample numbers of Toe-Off event from Left Foot FSR signal.&lt;br /&gt;
&lt;br /&gt;
* RF_HS - Sample numbers of Heel-Strike event  from Right Foot FSR signal. &lt;br /&gt;
&lt;br /&gt;
* RF_TO - Sample numbers of Toe-Off event from Right Foot FSR signal.&lt;br /&gt;
&lt;br /&gt;
[[File:GroundTruthStructure.png|1200px]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
=== 4. mainScript.m === &lt;br /&gt;
&lt;br /&gt;
This MATLAB script is an example on how to:&lt;br /&gt;
&lt;br /&gt;
* Read the accelerometer data of a subject for an activity &amp;amp; position&lt;br /&gt;
&lt;br /&gt;
* Read the Ground Truth Heel-Strike and Toe-Off gait events for the same&lt;br /&gt;
&lt;br /&gt;
* Plot all the signals&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
----&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 160%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;How to get the datasets?&amp;#039;&amp;#039;&amp;#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
&lt;br /&gt;
----------------------------------------------------------------------------------------------------------------------------------&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 110%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;1. Link to the Release Agreement and fill Registration Form:&amp;#039;&amp;#039;&amp;#039;  [http://islab.hh.se/mediawiki/Gait_database:_Release_agreement Click_Release Agreement]&amp;lt;/div&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 110%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;2. Link to download the datasets:&amp;#039;&amp;#039;&amp;#039;  Will be sent by Email after Registration Procedure is completed &amp;lt;/div&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
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&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 160%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;References&amp;#039;&amp;#039;&amp;#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
----------------------------------------------------&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 120%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;Please remember to include an appropriate citation to acknowledge the use of the database in all documents and papers that uses the MAREA Gait Database:&amp;#039;&amp;#039;&amp;#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
1. Siddhartha Khandelwal; Nicholas Wickström, &amp;quot;Evaluation of the performance of accelerometer-based gait event detection algorithms in different real-world scenarios using the MAREA gait database,&amp;quot; in Gait &amp;amp; Posture, DOI: 10.1016/j.gaitpost.2016.09.023, 2016&lt;br /&gt;
&lt;br /&gt;
Sciencedirect: [http://www.sciencedirect.com/science/article/pii/S0966636216305859]&lt;br /&gt;
Diva: [http://hh.diva-portal.org/smash/record.jsf?searchId=1&amp;amp;pid=diva2%3A999938&amp;amp;dswid=1416]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
2. Siddhartha Khandelwal; Nicholas Wickström, &amp;quot;Gait Event Detection in Real-World Environment for Long-Term Applications: Incorporating Domain Knowledge into Time-Frequency Analysis,&amp;quot; in IEEE Transactions on Neural Systems and Rehabilitation Engineering , vol.PP, no.99, pp.1-1, 2016 &lt;br /&gt;
&lt;br /&gt;
IEEE url: [http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7423805&amp;amp;newsearch=true&amp;amp;queryText=siddhartha%20khandelwal]&lt;br /&gt;
DiVA url: [http://hh.diva-portal.org/smash/record.jsf?searchId=1&amp;amp;pid=diva2:909015]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 120%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;You may also want to read:&amp;#039;&amp;#039;&amp;#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
3. Siddhartha Khandelwal; Nicholas Wickström, &amp;quot;Identification of Gait Events using Expert Knowledge and Continuous Wavelet Transform Analysis&amp;quot;, in BIOSIGNALS 2014, 7th International Conference on Bio-inspired Systems and Signal Processing, Angers, France, March 3-6, 2014 &lt;br /&gt;
&lt;br /&gt;
DiVA url: [http://hh.diva-portal.org/smash/record.jsf?searchId=1&amp;amp;pid=diva2:688909]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
4. Siddhartha Khandelwal, Nicholas Wickström, &amp;quot;Detecting Gait Events from Outdoor Accelerometer Data for Long-term and Continuous Monitoring Applications&amp;quot; in 13th International Symposium on 3D Analysis of Human Movement (3D-AHM 2014), 14–17 July, 2014, Lausanne, Switzerland.&lt;br /&gt;
&lt;br /&gt;
DiVA url: [http://www.diva-portal.org/smash/record.jsf?pid=diva2:735193]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 160%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;Created By&amp;#039;&amp;#039;&amp;#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
----------------------------------------------------&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
[http://islab.hh.se/mediawiki/Siddhartha_Khandelwal Siddhartha Khandelwal]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
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&lt;br /&gt;
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&lt;br /&gt;
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&lt;br /&gt;
&lt;br /&gt;
----&lt;/div&gt;</summary>
		<author><name>Siddhartha</name></author>
	</entry>
	<entry>
		<id>https://mw.hh.se/caisr/index.php?title=Gait_database&amp;diff=3236</id>
		<title>Gait database</title>
		<link rel="alternate" type="text/html" href="https://mw.hh.se/caisr/index.php?title=Gait_database&amp;diff=3236"/>
		<updated>2016-10-03T11:50:06Z</updated>

		<summary type="html">&lt;p&gt;Siddhartha: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== MAREA (&amp;#039;&amp;#039;&amp;#039;M&amp;#039;&amp;#039;&amp;#039;ovement &amp;#039;&amp;#039;&amp;#039;A&amp;#039;&amp;#039;&amp;#039;nalysis in &amp;#039;&amp;#039;&amp;#039;R&amp;#039;&amp;#039;&amp;#039;eal-world &amp;#039;&amp;#039;&amp;#039;E&amp;#039;&amp;#039;&amp;#039;nvironments using &amp;#039;&amp;#039;&amp;#039;A&amp;#039;&amp;#039;&amp;#039;ccelerometers) Gait Database ==&lt;br /&gt;
&lt;br /&gt;
[[File:accPlacement.jpg|right|thumb|200px|caption|&amp;quot;Figure 1: Position and orientation of each accelerometer at the beginning of each experiment. The accelerometer at the right ankle is attached arbitrarily without any pre-defined orientation. The FSRs are instrumented into the shoe soles to collect the ground truth. The data from all sensors is sampled at a frequency of 128 Hz.&amp;quot;]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
The &amp;#039;&amp;#039;&amp;#039;MAREA&amp;#039;&amp;#039;&amp;#039; gait database comprises of gait activities in different real-world environments as shown in the table below. 20 healthy adults (12 males and 8 females, average age: 33.4 +- 7 years, average mass: 73.2 +- 10.9 kg, average height: 172.6 +- 9.5 cm) participated in the study that was approved by the Ethical Review Board of Lund, Sweden. Each subject had a 3-axes Shimmer3 (Shimmer Research, Dublin, Ireland) accelerometer (+- 8g) attached to their waist, left wrist and left and right ankles using elastic bands and velcro straps. Figure 1 shows the position and orientation of each accelerometer at the beginning of each experiment. On the waist, the accelerometer X and Y axes were pointing to the lateral and downward direction, respectively. On the wrist and left ankle, the Z axis was pointing in the lateral direction while the Y axis was pointing downward and was aligned with the limb longitudinal axis. In order to simulate a lesser controlled scenario, the accelerometer on right ankle was positioned such that the Y axis was pointing downward but the Z axis was marginally disturbed such that it was not exactly perpendicular to the sagittal plane. The subjects were provided shoes that were instrumented with piezo-electric force sensitive resistors (FSRs), fixed at the extreme ends of the sole in order to provide the ground truth values for HS and TO. An external expansion board was used to synchronously collect the data from the FSRs on each foot and the respective ankle accelerometer, at a sampling frequency of 128Hz, and stored locally on the Shimmer3 microSD card. Due to the lack of a centralized data logger, the waist accelerometer was not in perfect synchronization with the ankle accelerometer.&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
{| class=&amp;quot;wikitable&amp;quot;&lt;br /&gt;
! style=&amp;quot;text-align: center;&amp;quot; | Subjects&lt;br /&gt;
! style=&amp;quot;text-align: center;&amp;quot; | Environment&lt;br /&gt;
! style=&amp;quot;text-align: center;&amp;quot; | Activity&lt;br /&gt;
! style=&amp;quot;text-align: center;&amp;quot; | Speed&lt;br /&gt;
! style=&amp;quot;text-align: center;&amp;quot; | Duration&lt;br /&gt;
! style=&amp;quot;text-align: center;&amp;quot; | Short Description&lt;br /&gt;
|-&lt;br /&gt;
| rowspan=&amp;quot;4&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | 11&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | Treadmill (flat)&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | Walk &amp;amp; run&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | 4km/hr - 8km/hr; increasing in steps&lt;br /&gt;
 of 0.4km/hr every minute&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | 10 min&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | Start walking and switch to &lt;br /&gt;
running at self-selected speed&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | Treadmill (slope)&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | Walk&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | Self-selected&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | 12 min&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | Treadmill is set to (5, 0, 10, 0, 15, 0) degree&lt;br /&gt;
inclinations with 2 mins at each angle&lt;br /&gt;
|-&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Indoor flat space&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Walk &amp;amp; run&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Self-selected&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | 6 min&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Start walking and switch &lt;br /&gt;
to running after 3 mins&lt;br /&gt;
|-&lt;br /&gt;
 |&lt;br /&gt;
|-&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | 9&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Outdoor street&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Walk &amp;amp; run&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Self-selected&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | 6 min&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Start walking and switch &lt;br /&gt;
to running after 3 mins&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
|}&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
==&amp;#039;&amp;#039;&amp;#039;Explanation of the database files&amp;#039;&amp;#039;&amp;#039; (download link below)==&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
=== 1. Activity Timings === &lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
There are two matrices provided, namely, &amp;#039;&amp;#039;&amp;#039;Indoor Experiment timings&amp;#039;&amp;#039;&amp;#039; and  &amp;#039;&amp;#039;&amp;#039;Outdoor Experiment timings&amp;#039;&amp;#039;&amp;#039; which contain the timing information of the activities and are included in a folder called Activity Timings.zip: &lt;br /&gt;
&lt;br /&gt;
[[File:ActivityTimingsGraph.png|right|thumb|500px|caption|&amp;quot;Example of how to use the sample numbers given in Indoor Experiment Timings matrix to extract desired activity data. This figure shows the entire Indoor data collected from accelerometer positioned at Right Ankle for Subject 5. &amp;quot;]]&lt;br /&gt;
&lt;br /&gt;
* &amp;#039;&amp;#039;&amp;#039;Indoor Experiment Timings&amp;#039;&amp;#039;&amp;#039;: This 11(rows) x 8(columns) matrix consists of &amp;#039;&amp;#039;&amp;#039;sample numbers&amp;#039;&amp;#039;&amp;#039; corresponding to the start and end of an activity, for a given subject, for the &amp;#039;&amp;#039;&amp;#039;Indoor Experiments&amp;#039;&amp;#039;&amp;#039;, i.e. &lt;br /&gt;
**Treadmill (flat)&lt;br /&gt;
**Treadmill (slope) &lt;br /&gt;
**Indoor flat space. &lt;br /&gt;
The figure below explains how to extract the sample nos. corresponding to an activity for a given subject, from the Indoor Experiment timings matrix.  &lt;br /&gt;
&lt;br /&gt;
[[File:Indoor Table.png|700px]]&lt;br /&gt;
&lt;br /&gt;
* &amp;#039;&amp;#039;&amp;#039;Outdoor Experiment Timings&amp;#039;&amp;#039;&amp;#039;: This 9(rows) x 3(columns) matrix consists of &amp;#039;&amp;#039;&amp;#039;sample numbers&amp;#039;&amp;#039;&amp;#039; corresponding to the start and end of an activity, for a given subject, for the &amp;#039;&amp;#039;&amp;#039;Outdoor Street Experiments&amp;#039;&amp;#039;&amp;#039;. The figure below explains how to extract the sample nos. corresponding to an activity for a given subject, from the Outdoor Experiment timings matrix.&lt;br /&gt;
&lt;br /&gt;
[[File:Outdoor Table.png|800px]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
=== 2. Subject Data === &lt;br /&gt;
&lt;br /&gt;
[[File:AccXYZ.png|right|thumb|400px|caption|&amp;quot;Figure 2: Example of the accelerometer data from the X, Y and Z axis for Sub5_RF&amp;quot;]]&lt;br /&gt;
&lt;br /&gt;
The Subject data files are provided in two formats, namely, .mat format (&amp;#039;&amp;#039;&amp;#039;Subject Data_mat format.zip&amp;#039;&amp;#039;&amp;#039;) and .txt format &amp;#039;&amp;#039;&amp;#039;Subject Data_txt format).zip&amp;#039;&amp;#039;&amp;#039;. The naming convention of the files is: Sub&amp;lt;number&amp;gt;_&amp;lt;position&amp;gt;, where:&lt;br /&gt;
*&amp;lt;number&amp;gt;: stands for Subject number and ranges from 1 to 20. &lt;br /&gt;
*&amp;lt;position&amp;gt;: stands for the position of the accelerometer on the body as shown in Figure 1. The positions are:&lt;br /&gt;
**LF - Left Ankle&lt;br /&gt;
**RF - Right Ankle&lt;br /&gt;
**Wrist&lt;br /&gt;
**Waist&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
For each Subject file, eg. Sub5_RF, the accelerometer data from the 3 axis accelerometer is stored in 3 columns (separated using comma in the .txt files), each named as: &lt;br /&gt;
*accX - data from X - axis&lt;br /&gt;
*accY - data from Y - axis&lt;br /&gt;
*accZ - data from Z - axis&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
===3. Ground Truth===&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
The Ground Truth is provided as a 11x7 structure that gives the timing of the Heel-Strike and Toe-Off events extracted from the FSR signals. This timing information is provided in terms of sample numbers and is relative to each activity. The structure consists of 7 fields that have &amp;#039;&amp;#039;&amp;#039;already been segregated&amp;#039;&amp;#039;&amp;#039; using the Indoor Experiment Timings and Outdoor Experiment Timings explained above: &lt;br /&gt;
*treadWalk       - treadmill (flat) walk &lt;br /&gt;
&lt;br /&gt;
*treadIncline    - treadmill (slope) walk&lt;br /&gt;
&lt;br /&gt;
*treadWalknRun   - treadmill (flat) walk &amp;amp; run&lt;br /&gt;
&lt;br /&gt;
*indoorWalk      - indoor (flat space) walk&lt;br /&gt;
&lt;br /&gt;
*indoorWalknRun  - indoor (flat space) walk &amp;amp; run&lt;br /&gt;
&lt;br /&gt;
*outdoorWalk     - outdoor street walk&lt;br /&gt;
&lt;br /&gt;
*outdoorWalknRun - outdoor street walk &amp;amp; run &lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
Each row of the structure represents a Subject. For Indoor activities, Row 1 -&amp;gt; Subject1, ..., Row 11 -&amp;gt; Subject 11 and so on. For outdoor activities, Row 1 -&amp;gt; Subject 12,..., Row 9 -&amp;gt; Subject 20. As an example: GroundTruth(1).treadWalk provides the ground truth data for Subject 1 for treadmill (flat) walk. This is a structure with fields: &lt;br /&gt;
* SubIdx - Subject number. Ranges from Sub1 - Sub20&lt;br /&gt;
&lt;br /&gt;
* LF_HS - Sample numbers of Heel-Strike event from Left Foot FSR signal.&lt;br /&gt;
&lt;br /&gt;
* LF_TO - Sample numbers of Toe-Off event from Left Foot FSR signal.&lt;br /&gt;
&lt;br /&gt;
* RF_HS - Sample numbers of Heel-Strike event  from Right Foot FSR signal. &lt;br /&gt;
&lt;br /&gt;
* RF_TO - Sample numbers of Toe-Off event from Right Foot FSR signal.&lt;br /&gt;
&lt;br /&gt;
[[File:GroundTruthStructure.png|1200px]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
=== 4. mainScript.m === &lt;br /&gt;
&lt;br /&gt;
This MATLAB script is an example on how to:&lt;br /&gt;
&lt;br /&gt;
* Read the accelerometer data of a subject for an activity &amp;amp; position&lt;br /&gt;
&lt;br /&gt;
* Read the Ground Truth Heel-Strike and Toe-Off gait events for the same&lt;br /&gt;
&lt;br /&gt;
* Plot all the signals&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
----&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 160%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;How to get the datasets?&amp;#039;&amp;#039;&amp;#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
&lt;br /&gt;
----------------------------------------------------------------------------------------------------------------------------------&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 110%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;1. Link to the Release Agreement and fill Registration Form:&amp;#039;&amp;#039;&amp;#039;  [http://islab.hh.se/mediawiki/Gait_database:_Release_agreement Click_Release Agreement]&amp;lt;/div&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 110%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;2. Link to download the datasets:&amp;#039;&amp;#039;&amp;#039;  Will be sent by Email after Registration Procedure is completed &amp;lt;/div&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 160%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;References&amp;#039;&amp;#039;&amp;#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
----------------------------------------------------&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 120%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;Please remember to include an appropriate citation to acknowledge the use of the database in all documents and papers that uses the MAREA Gait Database:&amp;#039;&amp;#039;&amp;#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
1. Siddhartha Khandelwal; Nicholas Wickström, &amp;quot;Evaluation of the performance of accelerometer-based gait event detection algorithms in different real-world scenarios using the MAREA gait database,&amp;quot; in Gait &amp;amp; Posture, DOI: 10.1016/j.gaitpost.2016.09.023, 2016&lt;br /&gt;
&lt;br /&gt;
Sciencedirect: [http://www.sciencedirect.com/science/article/pii/S0966636216305859]&lt;br /&gt;
Diva: [http://hh.diva-portal.org/smash/record.jsf?searchId=1&amp;amp;pid=diva2%3A999938&amp;amp;dswid=1416]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
2. Siddhartha Khandelwal; Nicholas Wickström, &amp;quot;Gait Event Detection in Real-World Environment for Long-Term Applications: Incorporating Domain Knowledge into Time-Frequency Analysis,&amp;quot; in IEEE Transactions on Neural Systems and Rehabilitation Engineering , vol.PP, no.99, pp.1-1, 2016 &lt;br /&gt;
&lt;br /&gt;
IEEE url: [http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7423805&amp;amp;newsearch=true&amp;amp;queryText=siddhartha%20khandelwal]&lt;br /&gt;
DiVA url: [http://hh.diva-portal.org/smash/record.jsf?searchId=1&amp;amp;pid=diva2:909015]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 120%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;You may also want to read:&amp;#039;&amp;#039;&amp;#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
3. Siddhartha Khandelwal; Nicholas Wickström, &amp;quot;Identification of Gait Events using Expert Knowledge and Continuous Wavelet Transform Analysis&amp;quot;, in BIOSIGNALS 2014, 7th International Conference on Bio-inspired Systems and Signal Processing, Angers, France, March 3-6, 2014 &lt;br /&gt;
&lt;br /&gt;
DiVA url: [http://hh.diva-portal.org/smash/record.jsf?searchId=1&amp;amp;pid=diva2:688909]&lt;br /&gt;
&lt;br /&gt;
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&amp;lt;div style=&amp;quot;font-size: 160%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;Created By&amp;#039;&amp;#039;&amp;#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
----------------------------------------------------&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
[http://islab.hh.se/mediawiki/Siddhartha_Khandelwal Siddhartha Khandelwal]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
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----&lt;/div&gt;</summary>
		<author><name>Siddhartha</name></author>
	</entry>
	<entry>
		<id>https://mw.hh.se/caisr/index.php?title=Gait_database&amp;diff=3235</id>
		<title>Gait database</title>
		<link rel="alternate" type="text/html" href="https://mw.hh.se/caisr/index.php?title=Gait_database&amp;diff=3235"/>
		<updated>2016-10-03T11:45:35Z</updated>

		<summary type="html">&lt;p&gt;Siddhartha: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== MAREA (&amp;#039;&amp;#039;&amp;#039;M&amp;#039;&amp;#039;&amp;#039;ovement &amp;#039;&amp;#039;&amp;#039;A&amp;#039;&amp;#039;&amp;#039;nalysis in &amp;#039;&amp;#039;&amp;#039;R&amp;#039;&amp;#039;&amp;#039;eal-world &amp;#039;&amp;#039;&amp;#039;E&amp;#039;&amp;#039;&amp;#039;nvironments using &amp;#039;&amp;#039;&amp;#039;A&amp;#039;&amp;#039;&amp;#039;ccelerometers) Gait Database ==&lt;br /&gt;
&lt;br /&gt;
[[File:accPlacement.jpg|right|thumb|200px|caption|&amp;quot;Figure 1: Position and orientation of each accelerometer at the beginning of each experiment. The accelerometer at the right ankle is attached arbitrarily without any pre-defined orientation. The FSRs are instrumented into the shoe soles to collect the ground truth. The data from all sensors is sampled at a frequency of 128 Hz.&amp;quot;]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
The &amp;#039;&amp;#039;&amp;#039;MAREA&amp;#039;&amp;#039;&amp;#039; gait database comprises of gait activities in different real-world environments as shown in the table below. 20 healthy adults (12 males and 8 females, average age: 33.4 +- 7 years, average mass: 73.2 +- 10.9 kg, average height: 172.6 +- 9.5 cm) participated in the study that was approved by the Ethical Review Board of Lund, Sweden. Each subject had a 3-axes Shimmer3 (Shimmer Research, Dublin, Ireland) accelerometer (+- 8g) attached to their waist, left wrist and left and right ankles using elastic bands and velcro straps. Figure 1 shows the position and orientation of each accelerometer at the beginning of each experiment. On the waist, the accelerometer X and Y axes were pointing to the lateral and downward direction, respectively. On the wrist and left ankle, the Z axis was pointing in the lateral direction while the Y axis was pointing downward and was aligned with the limb longitudinal axis. In order to simulate a lesser controlled scenario, the accelerometer on right ankle was positioned such that the Y axis was pointing downward but the Z axis was marginally disturbed such that it was not exactly perpendicular to the sagittal plane. The subjects were provided shoes that were instrumented with piezo-electric force sensitive resistors (FSRs), fixed at the extreme ends of the sole in order to provide the ground truth values for HS and TO. An external expansion board was used to synchronously collect the data from the FSRs on each foot and the respective ankle accelerometer, at a sampling frequency of 128Hz, and stored locally on the Shimmer3 microSD card. Due to the lack of a centralized data logger, the waist accelerometer was not in perfect synchronization with the ankle accelerometer.&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
{| class=&amp;quot;wikitable&amp;quot;&lt;br /&gt;
! style=&amp;quot;text-align: center;&amp;quot; | Subjects&lt;br /&gt;
! style=&amp;quot;text-align: center;&amp;quot; | Environment&lt;br /&gt;
! style=&amp;quot;text-align: center;&amp;quot; | Activity&lt;br /&gt;
! style=&amp;quot;text-align: center;&amp;quot; | Speed&lt;br /&gt;
! style=&amp;quot;text-align: center;&amp;quot; | Duration&lt;br /&gt;
! style=&amp;quot;text-align: center;&amp;quot; | Short Description&lt;br /&gt;
|-&lt;br /&gt;
| rowspan=&amp;quot;4&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | 11&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | Treadmill (flat)&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | Walk &amp;amp; run&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | 4km/hr - 8km/hr; increasing in steps&lt;br /&gt;
 of 0.4km/hr every minute&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | 10 min&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | Start walking and switch to &lt;br /&gt;
running at self-selected speed&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | Treadmill (slope)&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | Walk&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | Self-selected&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | 12 min&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | Treadmill is set to (5, 0, 10, 0, 15, 0) degree&lt;br /&gt;
inclinations with 2 mins at each angle&lt;br /&gt;
|-&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Indoor flat space&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Walk &amp;amp; run&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Self-selected&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | 6 min&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Start walking and switch &lt;br /&gt;
to running after 3 mins&lt;br /&gt;
|-&lt;br /&gt;
 |&lt;br /&gt;
|-&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | 9&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Outdoor street&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Walk &amp;amp; run&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Self-selected&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | 6 min&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Start walking and switch &lt;br /&gt;
to running after 3 mins&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
|}&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
==&amp;#039;&amp;#039;&amp;#039;Explanation of the database files&amp;#039;&amp;#039;&amp;#039; (download link below)==&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
=== 1. Activity Timings === &lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
There are two matrices provided, namely, &amp;#039;&amp;#039;&amp;#039;Indoor Experiment timings&amp;#039;&amp;#039;&amp;#039; and  &amp;#039;&amp;#039;&amp;#039;Outdoor Experiment timings&amp;#039;&amp;#039;&amp;#039; which contain the timing information of the activities and are included in a folder called Activity Timings.zip: &lt;br /&gt;
&lt;br /&gt;
[[File:ActivityTimingsGraph.png|right|thumb|500px|caption|&amp;quot;Example of how to use the sample numbers given in Indoor Experiment Timings matrix to extract desired activity data. This figure shows the entire Indoor data collected from accelerometer positioned at Right Ankle for Subject 5. &amp;quot;]]&lt;br /&gt;
&lt;br /&gt;
* &amp;#039;&amp;#039;&amp;#039;Indoor Experiment Timings&amp;#039;&amp;#039;&amp;#039;: This 11(rows) x 8(columns) matrix consists of &amp;#039;&amp;#039;&amp;#039;sample numbers&amp;#039;&amp;#039;&amp;#039; corresponding to the start and end of an activity, for a given subject, for the &amp;#039;&amp;#039;&amp;#039;Indoor Experiments&amp;#039;&amp;#039;&amp;#039;, i.e. &lt;br /&gt;
**Treadmill (flat)&lt;br /&gt;
**Treadmill (slope) &lt;br /&gt;
**Indoor flat space. &lt;br /&gt;
The figure below explains how to extract the sample nos. corresponding to an activity for a given subject, from the Indoor Experiment timings matrix.  &lt;br /&gt;
&lt;br /&gt;
[[File:Indoor Table.png|700px]]&lt;br /&gt;
&lt;br /&gt;
* &amp;#039;&amp;#039;&amp;#039;Outdoor Experiment Timings&amp;#039;&amp;#039;&amp;#039;: This 9(rows) x 3(columns) matrix consists of &amp;#039;&amp;#039;&amp;#039;sample numbers&amp;#039;&amp;#039;&amp;#039; corresponding to the start and end of an activity, for a given subject, for the &amp;#039;&amp;#039;&amp;#039;Outdoor Street Experiments&amp;#039;&amp;#039;&amp;#039;. The figure below explains how to extract the sample nos. corresponding to an activity for a given subject, from the Outdoor Experiment timings matrix.&lt;br /&gt;
&lt;br /&gt;
[[File:Outdoor Table.png|800px]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
=== 2. Subject Data === &lt;br /&gt;
&lt;br /&gt;
[[File:AccXYZ.png|right|thumb|400px|caption|&amp;quot;Figure 2: Example of the accelerometer data from the X, Y and Z axis for Sub5_RF&amp;quot;]]&lt;br /&gt;
&lt;br /&gt;
The Subject data files are provided in two formats, namely, .mat format (&amp;#039;&amp;#039;&amp;#039;Subject Data_mat format.zip&amp;#039;&amp;#039;&amp;#039;) and .txt format &amp;#039;&amp;#039;&amp;#039;Subject Data_txt format).zip&amp;#039;&amp;#039;&amp;#039;. The naming convention of the files is: Sub&amp;lt;number&amp;gt;_&amp;lt;position&amp;gt;, where:&lt;br /&gt;
*&amp;lt;number&amp;gt;: stands for Subject number and ranges from 1 to 20. &lt;br /&gt;
*&amp;lt;position&amp;gt;: stands for the position of the accelerometer on the body as shown in Figure 1. The positions are:&lt;br /&gt;
**LF - Left Ankle&lt;br /&gt;
**RF - Right Ankle&lt;br /&gt;
**Wrist&lt;br /&gt;
**Waist&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
For each Subject file, eg. Sub5_RF, the accelerometer data from the 3 axis accelerometer is stored in 3 columns (separated using comma in the .txt files), each named as: &lt;br /&gt;
*accX - data from X - axis&lt;br /&gt;
*accY - data from Y - axis&lt;br /&gt;
*accZ - data from Z - axis&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
===3. Ground Truth===&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
The Ground Truth is provided as a 11x7 structure that gives the timing of the Heel-Strike and Toe-Off events extracted from the FSR signals. This timing information is provided in terms of sample numbers and is relative to each activity. The structure consists of 7 fields that have &amp;#039;&amp;#039;&amp;#039;already been segregated&amp;#039;&amp;#039;&amp;#039; using the Indoor Experiment Timings and Outdoor Experiment Timings explained above: &lt;br /&gt;
*treadWalk       - treadmill (flat) walk &lt;br /&gt;
&lt;br /&gt;
*treadIncline    - treadmill (slope) walk&lt;br /&gt;
&lt;br /&gt;
*treadWalknRun   - treadmill (flat) walk &amp;amp; run&lt;br /&gt;
&lt;br /&gt;
*indoorWalk      - indoor (flat space) walk&lt;br /&gt;
&lt;br /&gt;
*indoorWalknRun  - indoor (flat space) walk &amp;amp; run&lt;br /&gt;
&lt;br /&gt;
*outdoorWalk     - outdoor street walk&lt;br /&gt;
&lt;br /&gt;
*outdoorWalknRun - outdoor street walk &amp;amp; run &lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
Each row of the structure represents a Subject. For Indoor activities, Row 1 -&amp;gt; Subject1, ..., Row 11 -&amp;gt; Subject 11 and so on. For outdoor activities, Row 1 -&amp;gt; Subject 12,..., Row 9 -&amp;gt; Subject 20. As an example: GroundTruth(1).treadWalk provides the ground truth data for Subject 1 for treadmill (flat) walk. This is a structure with fields: &lt;br /&gt;
* SubIdx - Subject number. Ranges from Sub1 - Sub20&lt;br /&gt;
&lt;br /&gt;
* LF_HS - Sample numbers of Heel-Strike event from Left Foot FSR signal.&lt;br /&gt;
&lt;br /&gt;
* LF_TO - Sample numbers of Toe-Off event from Left Foot FSR signal.&lt;br /&gt;
&lt;br /&gt;
* RF_HS - Sample numbers of Heel-Strike event  from Right Foot FSR signal. &lt;br /&gt;
&lt;br /&gt;
* RF_TO - Sample numbers of Toe-Off event from Right Foot FSR signal.&lt;br /&gt;
&lt;br /&gt;
[[File:GroundTruthStructure.png|1200px]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
=== 4. mainScript.m === &lt;br /&gt;
&lt;br /&gt;
This MATLAB script is an example on how to:&lt;br /&gt;
&lt;br /&gt;
* Read the accelerometer data of a subject for an activity &amp;amp; position&lt;br /&gt;
&lt;br /&gt;
* Read the Ground Truth Heel-Strike and Toe-Off gait events for the same&lt;br /&gt;
&lt;br /&gt;
* Plot all the signals&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
----&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 160%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;How to get the datasets?&amp;#039;&amp;#039;&amp;#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
&lt;br /&gt;
----------------------------------------------------------------------------------------------------------------------------------&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 110%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;1. Link to the Release Agreement and fill Registration Form:&amp;#039;&amp;#039;&amp;#039;  [http://islab.hh.se/mediawiki/Gait_database:_Release_agreement Click_Release Agreement]&amp;lt;/div&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 110%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;2. Link to download the datasets:&amp;#039;&amp;#039;&amp;#039;  Will be sent by Email after Registration Procedure is completed &amp;lt;/div&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
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&amp;lt;div style=&amp;quot;font-size: 160%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;References&amp;#039;&amp;#039;&amp;#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
----------------------------------------------------&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 120%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;Please remember to include an appropriate citation to acknowledge the use of the database in all documents and papers that uses the MAREA Gait Database:&amp;#039;&amp;#039;&amp;#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
1. Siddhartha Khandelwal; Nicholas Wickström, &amp;quot;Evaluation of the performance of accelerometer-based gait event detection algorithms in different real-world scenarios using the MAREA gait database,&amp;quot; in Gait &amp;amp; Posture, DOI: 10.1016/j.gaitpost.2016.09.023, 2016&lt;br /&gt;
&lt;br /&gt;
Sciencedirect: [http://www.sciencedirect.com/science/article/pii/S0966636216305859]&lt;br /&gt;
Diva: [http://www.diva-portal.org/smash/record.jsf?dswid=1789&amp;amp;pid=diva2%3A999938&amp;amp;c=1&amp;amp;searchType=SIMPLE&amp;amp;language=en&amp;amp;query=Evaluation+of+the+performance+of+accelerometer-based+gait+event+detection+algorithms+in+different+real-world+scenarios+using+the+MAREA+gait+database&amp;amp;af=%5B%5D&amp;amp;aq=%5B%5B%5D%5D&amp;amp;aq2=%5B%5B%5D%5D&amp;amp;aqe=%5B%5D&amp;amp;noOfRows=50&amp;amp;sortOrder=author_sort_asc&amp;amp;onlyFullText=false&amp;amp;sf=all]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
2. Siddhartha Khandelwal; Nicholas Wickström, &amp;quot;Gait Event Detection in Real-World Environment for Long-Term Applications: Incorporating Domain Knowledge into Time-Frequency Analysis,&amp;quot; in IEEE Transactions on Neural Systems and Rehabilitation Engineering , vol.PP, no.99, pp.1-1, 2016 &lt;br /&gt;
&lt;br /&gt;
IEEE url: [http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7423805&amp;amp;newsearch=true&amp;amp;queryText=siddhartha%20khandelwal]&lt;br /&gt;
DiVA url: [http://hh.diva-portal.org/smash/record.jsf?searchId=1&amp;amp;pid=diva2:909015]&lt;br /&gt;
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&amp;lt;div style=&amp;quot;font-size: 120%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;You may also want to read:&amp;#039;&amp;#039;&amp;#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
3. Siddhartha Khandelwal; Nicholas Wickström, &amp;quot;Identification of Gait Events using Expert Knowledge and Continuous Wavelet Transform Analysis&amp;quot;, in BIOSIGNALS 2014, 7th International Conference on Bio-inspired Systems and Signal Processing, Angers, France, March 3-6, 2014 &lt;br /&gt;
&lt;br /&gt;
DiVA url: [http://hh.diva-portal.org/smash/record.jsf?searchId=1&amp;amp;pid=diva2:688909]&lt;br /&gt;
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&amp;lt;div style=&amp;quot;font-size: 160%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;Created By&amp;#039;&amp;#039;&amp;#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
----------------------------------------------------&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
[http://islab.hh.se/mediawiki/Siddhartha_Khandelwal Siddhartha Khandelwal]&lt;br /&gt;
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----&lt;/div&gt;</summary>
		<author><name>Siddhartha</name></author>
	</entry>
	<entry>
		<id>https://mw.hh.se/caisr/index.php?title=Gait_database&amp;diff=3234</id>
		<title>Gait database</title>
		<link rel="alternate" type="text/html" href="https://mw.hh.se/caisr/index.php?title=Gait_database&amp;diff=3234"/>
		<updated>2016-10-03T11:44:28Z</updated>

		<summary type="html">&lt;p&gt;Siddhartha: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== MAREA (&amp;#039;&amp;#039;&amp;#039;M&amp;#039;&amp;#039;&amp;#039;ovement &amp;#039;&amp;#039;&amp;#039;A&amp;#039;&amp;#039;&amp;#039;nalysis in &amp;#039;&amp;#039;&amp;#039;R&amp;#039;&amp;#039;&amp;#039;eal-world &amp;#039;&amp;#039;&amp;#039;E&amp;#039;&amp;#039;&amp;#039;nvironments using &amp;#039;&amp;#039;&amp;#039;A&amp;#039;&amp;#039;&amp;#039;ccelerometers) Gait Database ==&lt;br /&gt;
&lt;br /&gt;
[[File:accPlacement.jpg|right|thumb|200px|caption|&amp;quot;Figure 1: Position and orientation of each accelerometer at the beginning of each experiment. The accelerometer at the right ankle is attached arbitrarily without any pre-defined orientation. The FSRs are instrumented into the shoe soles to collect the ground truth. The data from all sensors is sampled at a frequency of 128 Hz.&amp;quot;]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
The &amp;#039;&amp;#039;&amp;#039;MAREA&amp;#039;&amp;#039;&amp;#039; gait database comprises of gait activities in different real-world environments as shown in the table below. 20 healthy adults (12 males and 8 females, average age: 33.4 +- 7 years, average mass: 73.2 +- 10.9 kg, average height: 172.6 +- 9.5 cm) participated in the study that was approved by the Ethical Review Board of Lund, Sweden. Each subject had a 3-axes Shimmer3 (Shimmer Research, Dublin, Ireland) accelerometer (+- 8g) attached to their waist, left wrist and left and right ankles using elastic bands and velcro straps. Figure 1 shows the position and orientation of each accelerometer at the beginning of each experiment. On the waist, the accelerometer X and Y axes were pointing to the lateral and downward direction, respectively. On the wrist and left ankle, the Z axis was pointing in the lateral direction while the Y axis was pointing downward and was aligned with the limb longitudinal axis. In order to simulate a lesser controlled scenario, the accelerometer on right ankle was positioned such that the Y axis was pointing downward but the Z axis was marginally disturbed such that it was not exactly perpendicular to the sagittal plane. The subjects were provided shoes that were instrumented with piezo-electric force sensitive resistors (FSRs), fixed at the extreme ends of the sole in order to provide the ground truth values for HS and TO. An external expansion board was used to synchronously collect the data from the FSRs on each foot and the respective ankle accelerometer, at a sampling frequency of 128Hz, and stored locally on the Shimmer3 microSD card. Due to the lack of a centralized data logger, the waist accelerometer was not in perfect synchronization with the ankle accelerometer.&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
{| class=&amp;quot;wikitable&amp;quot;&lt;br /&gt;
! style=&amp;quot;text-align: center;&amp;quot; | Subjects&lt;br /&gt;
! style=&amp;quot;text-align: center;&amp;quot; | Environment&lt;br /&gt;
! style=&amp;quot;text-align: center;&amp;quot; | Activity&lt;br /&gt;
! style=&amp;quot;text-align: center;&amp;quot; | Speed&lt;br /&gt;
! style=&amp;quot;text-align: center;&amp;quot; | Duration&lt;br /&gt;
! style=&amp;quot;text-align: center;&amp;quot; | Short Description&lt;br /&gt;
|-&lt;br /&gt;
| rowspan=&amp;quot;4&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | 11&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | Treadmill (flat)&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | Walk &amp;amp; run&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | 4km/hr - 8km/hr; increasing in steps&lt;br /&gt;
 of 0.4km/hr every minute&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | 10 min&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | Start walking and switch to &lt;br /&gt;
running at self-selected speed&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | Treadmill (slope)&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | Walk&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | Self-selected&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | 12 min&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | Treadmill is set to (5, 0, 10, 0, 15, 0) degree&lt;br /&gt;
inclinations with 2 mins at each angle&lt;br /&gt;
|-&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Indoor flat space&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Walk &amp;amp; run&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Self-selected&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | 6 min&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Start walking and switch &lt;br /&gt;
to running after 3 mins&lt;br /&gt;
|-&lt;br /&gt;
 |&lt;br /&gt;
|-&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | 9&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Outdoor street&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Walk &amp;amp; run&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Self-selected&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | 6 min&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Start walking and switch &lt;br /&gt;
to running after 3 mins&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
|}&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
==&amp;#039;&amp;#039;&amp;#039;Explanation of the database files&amp;#039;&amp;#039;&amp;#039; (download link below)==&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
=== 1. Activity Timings === &lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
There are two matrices provided, namely, &amp;#039;&amp;#039;&amp;#039;Indoor Experiment timings&amp;#039;&amp;#039;&amp;#039; and  &amp;#039;&amp;#039;&amp;#039;Outdoor Experiment timings&amp;#039;&amp;#039;&amp;#039; which contain the timing information of the activities and are included in a folder called Activity Timings.zip: &lt;br /&gt;
&lt;br /&gt;
[[File:ActivityTimingsGraph.png|right|thumb|500px|caption|&amp;quot;Example of how to use the sample numbers given in Indoor Experiment Timings matrix to extract desired activity data. This figure shows the entire Indoor data collected from accelerometer positioned at Right Ankle for Subject 5. &amp;quot;]]&lt;br /&gt;
&lt;br /&gt;
* &amp;#039;&amp;#039;&amp;#039;Indoor Experiment Timings&amp;#039;&amp;#039;&amp;#039;: This 11(rows) x 8(columns) matrix consists of &amp;#039;&amp;#039;&amp;#039;sample numbers&amp;#039;&amp;#039;&amp;#039; corresponding to the start and end of an activity, for a given subject, for the &amp;#039;&amp;#039;&amp;#039;Indoor Experiments&amp;#039;&amp;#039;&amp;#039;, i.e. &lt;br /&gt;
**Treadmill (flat)&lt;br /&gt;
**Treadmill (slope) &lt;br /&gt;
**Indoor flat space. &lt;br /&gt;
The figure below explains how to extract the sample nos. corresponding to an activity for a given subject, from the Indoor Experiment timings matrix.  &lt;br /&gt;
&lt;br /&gt;
[[File:Indoor Table.png|700px]]&lt;br /&gt;
&lt;br /&gt;
* &amp;#039;&amp;#039;&amp;#039;Outdoor Experiment Timings&amp;#039;&amp;#039;&amp;#039;: This 9(rows) x 3(columns) matrix consists of &amp;#039;&amp;#039;&amp;#039;sample numbers&amp;#039;&amp;#039;&amp;#039; corresponding to the start and end of an activity, for a given subject, for the &amp;#039;&amp;#039;&amp;#039;Outdoor Street Experiments&amp;#039;&amp;#039;&amp;#039;. The figure below explains how to extract the sample nos. corresponding to an activity for a given subject, from the Outdoor Experiment timings matrix.&lt;br /&gt;
&lt;br /&gt;
[[File:Outdoor Table.png|800px]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
=== 2. Subject Data === &lt;br /&gt;
&lt;br /&gt;
[[File:AccXYZ.png|right|thumb|400px|caption|&amp;quot;Figure 2: Example of the accelerometer data from the X, Y and Z axis for Sub5_RF&amp;quot;]]&lt;br /&gt;
&lt;br /&gt;
The Subject data files are provided in two formats, namely, .mat format (&amp;#039;&amp;#039;&amp;#039;Subject Data_mat format.zip&amp;#039;&amp;#039;&amp;#039;) and .txt format &amp;#039;&amp;#039;&amp;#039;Subject Data_txt format).zip&amp;#039;&amp;#039;&amp;#039;. The naming convention of the files is: Sub&amp;lt;number&amp;gt;_&amp;lt;position&amp;gt;, where:&lt;br /&gt;
*&amp;lt;number&amp;gt;: stands for Subject number and ranges from 1 to 20. &lt;br /&gt;
*&amp;lt;position&amp;gt;: stands for the position of the accelerometer on the body as shown in Figure 1. The positions are:&lt;br /&gt;
**LF - Left Ankle&lt;br /&gt;
**RF - Right Ankle&lt;br /&gt;
**Wrist&lt;br /&gt;
**Waist&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
For each Subject file, eg. Sub5_RF, the accelerometer data from the 3 axis accelerometer is stored in 3 columns (separated using comma in the .txt files), each named as: &lt;br /&gt;
*accX - data from X - axis&lt;br /&gt;
*accY - data from Y - axis&lt;br /&gt;
*accZ - data from Z - axis&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
===3. Ground Truth===&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
The Ground Truth is provided as a 11x7 structure that gives the timing of the Heel-Strike and Toe-Off events extracted from the FSR signals. This timing information is provided in terms of sample numbers and is relative to each activity. The structure consists of 7 fields that have &amp;#039;&amp;#039;&amp;#039;already been segregated&amp;#039;&amp;#039;&amp;#039; using the Indoor Experiment Timings and Outdoor Experiment Timings explained above: &lt;br /&gt;
*treadWalk       - treadmill (flat) walk &lt;br /&gt;
&lt;br /&gt;
*treadIncline    - treadmill (slope) walk&lt;br /&gt;
&lt;br /&gt;
*treadWalknRun   - treadmill (flat) walk &amp;amp; run&lt;br /&gt;
&lt;br /&gt;
*indoorWalk      - indoor (flat space) walk&lt;br /&gt;
&lt;br /&gt;
*indoorWalknRun  - indoor (flat space) walk &amp;amp; run&lt;br /&gt;
&lt;br /&gt;
*outdoorWalk     - outdoor street walk&lt;br /&gt;
&lt;br /&gt;
*outdoorWalknRun - outdoor street walk &amp;amp; run &lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
Each row of the structure represents a Subject. For Indoor activities, Row 1 -&amp;gt; Subject1, ..., Row 11 -&amp;gt; Subject 11 and so on. For outdoor activities, Row 1 -&amp;gt; Subject 12,..., Row 9 -&amp;gt; Subject 20. As an example: GroundTruth(1).treadWalk provides the ground truth data for Subject 1 for treadmill (flat) walk. This is a structure with fields: &lt;br /&gt;
* SubIdx - Subject number. Ranges from Sub1 - Sub20&lt;br /&gt;
&lt;br /&gt;
* LF_HS - Sample numbers of Heel-Strike event from Left Foot FSR signal.&lt;br /&gt;
&lt;br /&gt;
* LF_TO - Sample numbers of Toe-Off event from Left Foot FSR signal.&lt;br /&gt;
&lt;br /&gt;
* RF_HS - Sample numbers of Heel-Strike event  from Right Foot FSR signal. &lt;br /&gt;
&lt;br /&gt;
* RF_TO - Sample numbers of Toe-Off event from Right Foot FSR signal.&lt;br /&gt;
&lt;br /&gt;
[[File:GroundTruthStructure.png|1200px]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
=== 4. mainScript.m === &lt;br /&gt;
&lt;br /&gt;
This MATLAB script is an example on how to:&lt;br /&gt;
&lt;br /&gt;
* Read the accelerometer data of a subject for an activity &amp;amp; position&lt;br /&gt;
&lt;br /&gt;
* Read the Ground Truth Heel-Strike and Toe-Off gait events for the same&lt;br /&gt;
&lt;br /&gt;
* Plot all the signals&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
----&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 160%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;How to get the datasets?&amp;#039;&amp;#039;&amp;#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
&lt;br /&gt;
----------------------------------------------------------------------------------------------------------------------------------&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 110%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;1. Link to the Release Agreement and fill Registration Form:&amp;#039;&amp;#039;&amp;#039;  [http://islab.hh.se/mediawiki/Gait_database:_Release_agreement Click_Release Agreement]&amp;lt;/div&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 110%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;2. Link to download the datasets:&amp;#039;&amp;#039;&amp;#039;  Will be sent by Email after Registration Procedure is completed &amp;lt;/div&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 160%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;References&amp;#039;&amp;#039;&amp;#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
----------------------------------------------------&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 120%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;Please remember to include an appropriate citation to acknowledge the use of the database in all documents and papers that uses the MAREA Gait Database:&amp;#039;&amp;#039;&amp;#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
1. Siddhartha Khandelwal; Nicholas Wickström, &amp;quot;Evaluation of the performance of accelerometer-based gait event detection algorithms in different real-world scenarios using the MAREA gait database,&amp;quot; in Gait &amp;amp; Posture, DOI: 10.1016/j.gaitpost.2016.09.023, 2016&lt;br /&gt;
&lt;br /&gt;
Sciencedirect: [http://www.sciencedirect.com/science/article/pii/S0966636216305859]&lt;br /&gt;
Diva: [http://www.diva-portal.org/smash/record.jsf?dswid=1789&amp;amp;pid=diva2%3A999938&amp;amp;c=1&amp;amp;searchType=SIMPLE&amp;amp;language=en&amp;amp;query=Evaluation+of+the+performance+of+accelerometer-based+gait+event+detection+algorithms+in+different+real-world+scenarios+using+the+MAREA+gait+database&amp;amp;af=%5B%5D&amp;amp;aq=%5B%5B%5D%5D&amp;amp;aq2=%5B%5B%5D%5D&amp;amp;aqe=%5B%5D&amp;amp;noOfRows=50&amp;amp;sortOrder=author_sort_asc&amp;amp;onlyFullText=false&amp;amp;sf=all]&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 120%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;You may also want to read:&amp;#039;&amp;#039;&amp;#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
2. Siddhartha Khandelwal; Nicholas Wickström, &amp;quot;Gait Event Detection in Real-World Environment for Long-Term Applications: Incorporating Domain Knowledge into Time-Frequency Analysis,&amp;quot; in IEEE Transactions on Neural Systems and Rehabilitation Engineering , vol.PP, no.99, pp.1-1, 2016 &lt;br /&gt;
&lt;br /&gt;
IEEE url: [http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7423805&amp;amp;newsearch=true&amp;amp;queryText=siddhartha%20khandelwal]&lt;br /&gt;
DiVA url: [http://hh.diva-portal.org/smash/record.jsf?searchId=1&amp;amp;pid=diva2:909015]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
3. Siddhartha Khandelwal; Nicholas Wickström, &amp;quot;Identification of Gait Events using Expert Knowledge and Continuous Wavelet Transform Analysis&amp;quot;, in BIOSIGNALS 2014, 7th International Conference on Bio-inspired Systems and Signal Processing, Angers, France, March 3-6, 2014 &lt;br /&gt;
&lt;br /&gt;
DiVA url: [http://hh.diva-portal.org/smash/record.jsf?searchId=1&amp;amp;pid=diva2:688909]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 160%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;Created By&amp;#039;&amp;#039;&amp;#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
----------------------------------------------------&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
[http://islab.hh.se/mediawiki/Siddhartha_Khandelwal Siddhartha Khandelwal]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
----&lt;/div&gt;</summary>
		<author><name>Siddhartha</name></author>
	</entry>
	<entry>
		<id>https://mw.hh.se/caisr/index.php?title=Gait_database:_Release_agreement&amp;diff=3233</id>
		<title>Gait database: Release agreement</title>
		<link rel="alternate" type="text/html" href="https://mw.hh.se/caisr/index.php?title=Gait_database:_Release_agreement&amp;diff=3233"/>
		<updated>2016-10-03T11:27:12Z</updated>

		<summary type="html">&lt;p&gt;Siddhartha: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== Introduction ==&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
The MAREA Gait Database is meant to further research in gait analysis and related fields.  &lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== Release of the Database == &lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
This database can be downloaded as a zip file with password protection which shall be issued on a case-by-case basis. To receive the password, the requester must read and agree to the terms of usage of the database and fill the registration form. The form shall be evaluated and a link to download the database along with the password will be sent to the registered email address. Failure to observe this procedure may result in access being denied for the database.&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== Terms of Usage == &lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
The researcher(s) agrees to the following terms and conditions on the MAREA Gait Database:&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
1. &amp;#039;&amp;#039;&amp;#039;Redistribution&amp;#039;&amp;#039;&amp;#039;: The database, in whole or in part, will not be further distributed, published, copied, or disseminated in any way or form whatsoever, whether for profit or not. This&lt;br /&gt;
includes further distributing, copying or disseminating to a different facility or organizational unit in the requesting university, organization, or company.&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
2. &amp;#039;&amp;#039;&amp;#039;Modification and Commercial Use&amp;#039;&amp;#039;&amp;#039;: The database, in whole or in part, may not be modified or used for commercial purposes.&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
3. &amp;#039;&amp;#039;&amp;#039;Authorization&amp;#039;&amp;#039;&amp;#039;: All rights in and relating to the database remain with Halmstad University.&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
4. &amp;#039;&amp;#039;&amp;#039;Citation/Reference&amp;#039;&amp;#039;&amp;#039;: All documents and papers that report on research that uses the MAREA Gait Database will acknowledge&lt;br /&gt;
the use of the database by including an appropriate citation to the following:&lt;br /&gt;
	&lt;br /&gt;
&amp;#039;&amp;#039;Siddhartha Khandelwal; Nicholas Wickström, &amp;quot;Evaluation of the performance of accelerometer-based gait event detection algorithms in different real-world scenarios using the MAREA gait database,&amp;quot; in Gait &amp;amp; Posture, DOI: 10.1016/j.gaitpost.2016.09.023, 2016&amp;#039;&amp;#039;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
6. &amp;#039;&amp;#039;&amp;#039;Publications&amp;#039;&amp;#039;&amp;#039;: A copy of all reports and papers that are for public or general release that use the database will be forwarded prior to or after release to Siddhartha Khandelwal (siddhartha.khandelwal@hh.se) and Nicholas Wickström (nicholas@hh.se).&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
7. &amp;#039;&amp;#039;&amp;#039;Indemnification&amp;#039;&amp;#039;&amp;#039;: Researcher agrees to indemnify, defend, and hold harmless Halmstad University, individually and collectively, from any and all losses, expenses, damages, demands and/or claims based upon any such injury or damage (real or alleged) and shall pay all damages, claims, judgments or expenses resulting from researcher’s use of the database.&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== Registration Form ==&lt;br /&gt;
&lt;br /&gt;
Link to fill the registration form: [https://goo.gl/forms/AFJhaP3qyUzBBnk63 click_registration form ]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
----&lt;/div&gt;</summary>
		<author><name>Siddhartha</name></author>
	</entry>
	<entry>
		<id>https://mw.hh.se/caisr/index.php?title=Gait_database&amp;diff=3232</id>
		<title>Gait database</title>
		<link rel="alternate" type="text/html" href="https://mw.hh.se/caisr/index.php?title=Gait_database&amp;diff=3232"/>
		<updated>2016-10-03T11:26:48Z</updated>

		<summary type="html">&lt;p&gt;Siddhartha: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== MAREA (&amp;#039;&amp;#039;&amp;#039;M&amp;#039;&amp;#039;&amp;#039;ovement &amp;#039;&amp;#039;&amp;#039;A&amp;#039;&amp;#039;&amp;#039;nalysis in &amp;#039;&amp;#039;&amp;#039;R&amp;#039;&amp;#039;&amp;#039;eal-world &amp;#039;&amp;#039;&amp;#039;E&amp;#039;&amp;#039;&amp;#039;nvironments using &amp;#039;&amp;#039;&amp;#039;A&amp;#039;&amp;#039;&amp;#039;ccelerometers) Gait Database ==&lt;br /&gt;
&lt;br /&gt;
[[File:accPlacement.jpg|right|thumb|200px|caption|&amp;quot;Figure 1: Position and orientation of each accelerometer at the beginning of each experiment. The accelerometer at the right ankle is attached arbitrarily without any pre-defined orientation. The FSRs are instrumented into the shoe soles to collect the ground truth. The data from all sensors is sampled at a frequency of 128 Hz.&amp;quot;]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
The &amp;#039;&amp;#039;&amp;#039;MAREA&amp;#039;&amp;#039;&amp;#039; gait database comprises of gait activities in different real-world environments as shown in the table below. 20 healthy adults (12 males and 8 females, average age: 33.4 +- 7 years, average mass: 73.2 +- 10.9 kg, average height: 172.6 +- 9.5 cm) participated in the study that was approved by the Ethical Review Board of Lund, Sweden. Each subject had a 3-axes Shimmer3 (Shimmer Research, Dublin, Ireland) accelerometer (+- 8g) attached to their waist, left wrist and left and right ankles using elastic bands and velcro straps. Figure 1 shows the position and orientation of each accelerometer at the beginning of each experiment. On the waist, the accelerometer X and Y axes were pointing to the lateral and downward direction, respectively. On the wrist and left ankle, the Z axis was pointing in the lateral direction while the Y axis was pointing downward and was aligned with the limb longitudinal axis. In order to simulate a lesser controlled scenario, the accelerometer on right ankle was positioned such that the Y axis was pointing downward but the Z axis was marginally disturbed such that it was not exactly perpendicular to the sagittal plane. The subjects were provided shoes that were instrumented with piezo-electric force sensitive resistors (FSRs), fixed at the extreme ends of the sole in order to provide the ground truth values for HS and TO. An external expansion board was used to synchronously collect the data from the FSRs on each foot and the respective ankle accelerometer, at a sampling frequency of 128Hz, and stored locally on the Shimmer3 microSD card. Due to the lack of a centralized data logger, the waist accelerometer was not in perfect synchronization with the ankle accelerometer.&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
{| class=&amp;quot;wikitable&amp;quot;&lt;br /&gt;
! style=&amp;quot;text-align: center;&amp;quot; | Subjects&lt;br /&gt;
! style=&amp;quot;text-align: center;&amp;quot; | Environment&lt;br /&gt;
! style=&amp;quot;text-align: center;&amp;quot; | Activity&lt;br /&gt;
! style=&amp;quot;text-align: center;&amp;quot; | Speed&lt;br /&gt;
! style=&amp;quot;text-align: center;&amp;quot; | Duration&lt;br /&gt;
! style=&amp;quot;text-align: center;&amp;quot; | Short Description&lt;br /&gt;
|-&lt;br /&gt;
| rowspan=&amp;quot;4&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | 11&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | Treadmill (flat)&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | Walk &amp;amp; run&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | 4km/hr - 8km/hr; increasing in steps&lt;br /&gt;
 of 0.4km/hr every minute&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | 10 min&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | Start walking and switch to &lt;br /&gt;
running at self-selected speed&lt;br /&gt;
|-&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | Treadmill (slope)&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | Walk&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | Self-selected&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | 12 min&lt;br /&gt;
| style=&amp;quot;text-align: center;&amp;quot; | Treadmill is set to (5, 0, 10, 0, 15, 0) degree&lt;br /&gt;
inclinations with 2 mins at each angle&lt;br /&gt;
|-&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Indoor flat space&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Walk &amp;amp; run&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Self-selected&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | 6 min&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Start walking and switch &lt;br /&gt;
to running after 3 mins&lt;br /&gt;
|-&lt;br /&gt;
 |&lt;br /&gt;
|-&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | 9&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Outdoor street&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Walk &amp;amp; run&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Self-selected&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | 6 min&lt;br /&gt;
| rowspan=&amp;quot;2&amp;quot; style=&amp;quot;text-align: center;&amp;quot; | Start walking and switch &lt;br /&gt;
to running after 3 mins&lt;br /&gt;
|-&lt;br /&gt;
&lt;br /&gt;
|}&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
==&amp;#039;&amp;#039;&amp;#039;Explanation of the database files&amp;#039;&amp;#039;&amp;#039; (download link below)==&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
=== 1. Activity Timings === &lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
There are two matrices provided, namely, &amp;#039;&amp;#039;&amp;#039;Indoor Experiment timings&amp;#039;&amp;#039;&amp;#039; and  &amp;#039;&amp;#039;&amp;#039;Outdoor Experiment timings&amp;#039;&amp;#039;&amp;#039; which contain the timing information of the activities and are included in a folder called Activity Timings.zip: &lt;br /&gt;
&lt;br /&gt;
[[File:ActivityTimingsGraph.png|right|thumb|500px|caption|&amp;quot;Example of how to use the sample numbers given in Indoor Experiment Timings matrix to extract desired activity data. This figure shows the entire Indoor data collected from accelerometer positioned at Right Ankle for Subject 5. &amp;quot;]]&lt;br /&gt;
&lt;br /&gt;
* &amp;#039;&amp;#039;&amp;#039;Indoor Experiment Timings&amp;#039;&amp;#039;&amp;#039;: This 11(rows) x 8(columns) matrix consists of &amp;#039;&amp;#039;&amp;#039;sample numbers&amp;#039;&amp;#039;&amp;#039; corresponding to the start and end of an activity, for a given subject, for the &amp;#039;&amp;#039;&amp;#039;Indoor Experiments&amp;#039;&amp;#039;&amp;#039;, i.e. &lt;br /&gt;
**Treadmill (flat)&lt;br /&gt;
**Treadmill (slope) &lt;br /&gt;
**Indoor flat space. &lt;br /&gt;
The figure below explains how to extract the sample nos. corresponding to an activity for a given subject, from the Indoor Experiment timings matrix.  &lt;br /&gt;
&lt;br /&gt;
[[File:Indoor Table.png|700px]]&lt;br /&gt;
&lt;br /&gt;
* &amp;#039;&amp;#039;&amp;#039;Outdoor Experiment Timings&amp;#039;&amp;#039;&amp;#039;: This 9(rows) x 3(columns) matrix consists of &amp;#039;&amp;#039;&amp;#039;sample numbers&amp;#039;&amp;#039;&amp;#039; corresponding to the start and end of an activity, for a given subject, for the &amp;#039;&amp;#039;&amp;#039;Outdoor Street Experiments&amp;#039;&amp;#039;&amp;#039;. The figure below explains how to extract the sample nos. corresponding to an activity for a given subject, from the Outdoor Experiment timings matrix.&lt;br /&gt;
&lt;br /&gt;
[[File:Outdoor Table.png|800px]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
=== 2. Subject Data === &lt;br /&gt;
&lt;br /&gt;
[[File:AccXYZ.png|right|thumb|400px|caption|&amp;quot;Figure 2: Example of the accelerometer data from the X, Y and Z axis for Sub5_RF&amp;quot;]]&lt;br /&gt;
&lt;br /&gt;
The Subject data files are provided in two formats, namely, .mat format (&amp;#039;&amp;#039;&amp;#039;Subject Data_mat format.zip&amp;#039;&amp;#039;&amp;#039;) and .txt format &amp;#039;&amp;#039;&amp;#039;Subject Data_txt format).zip&amp;#039;&amp;#039;&amp;#039;. The naming convention of the files is: Sub&amp;lt;number&amp;gt;_&amp;lt;position&amp;gt;, where:&lt;br /&gt;
*&amp;lt;number&amp;gt;: stands for Subject number and ranges from 1 to 20. &lt;br /&gt;
*&amp;lt;position&amp;gt;: stands for the position of the accelerometer on the body as shown in Figure 1. The positions are:&lt;br /&gt;
**LF - Left Ankle&lt;br /&gt;
**RF - Right Ankle&lt;br /&gt;
**Wrist&lt;br /&gt;
**Waist&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
For each Subject file, eg. Sub5_RF, the accelerometer data from the 3 axis accelerometer is stored in 3 columns (separated using comma in the .txt files), each named as: &lt;br /&gt;
*accX - data from X - axis&lt;br /&gt;
*accY - data from Y - axis&lt;br /&gt;
*accZ - data from Z - axis&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
===3. Ground Truth===&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
The Ground Truth is provided as a 11x7 structure that gives the timing of the Heel-Strike and Toe-Off events extracted from the FSR signals. This timing information is provided in terms of sample numbers and is relative to each activity. The structure consists of 7 fields that have &amp;#039;&amp;#039;&amp;#039;already been segregated&amp;#039;&amp;#039;&amp;#039; using the Indoor Experiment Timings and Outdoor Experiment Timings explained above: &lt;br /&gt;
*treadWalk       - treadmill (flat) walk &lt;br /&gt;
&lt;br /&gt;
*treadIncline    - treadmill (slope) walk&lt;br /&gt;
&lt;br /&gt;
*treadWalknRun   - treadmill (flat) walk &amp;amp; run&lt;br /&gt;
&lt;br /&gt;
*indoorWalk      - indoor (flat space) walk&lt;br /&gt;
&lt;br /&gt;
*indoorWalknRun  - indoor (flat space) walk &amp;amp; run&lt;br /&gt;
&lt;br /&gt;
*outdoorWalk     - outdoor street walk&lt;br /&gt;
&lt;br /&gt;
*outdoorWalknRun - outdoor street walk &amp;amp; run &lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
Each row of the structure represents a Subject. For Indoor activities, Row 1 -&amp;gt; Subject1, ..., Row 11 -&amp;gt; Subject 11 and so on. For outdoor activities, Row 1 -&amp;gt; Subject 12,..., Row 9 -&amp;gt; Subject 20. As an example: GroundTruth(1).treadWalk provides the ground truth data for Subject 1 for treadmill (flat) walk. This is a structure with fields: &lt;br /&gt;
* SubIdx - Subject number. Ranges from Sub1 - Sub20&lt;br /&gt;
&lt;br /&gt;
* LF_HS - Sample numbers of Heel-Strike event from Left Foot FSR signal.&lt;br /&gt;
&lt;br /&gt;
* LF_TO - Sample numbers of Toe-Off event from Left Foot FSR signal.&lt;br /&gt;
&lt;br /&gt;
* RF_HS - Sample numbers of Heel-Strike event  from Right Foot FSR signal. &lt;br /&gt;
&lt;br /&gt;
* RF_TO - Sample numbers of Toe-Off event from Right Foot FSR signal.&lt;br /&gt;
&lt;br /&gt;
[[File:GroundTruthStructure.png|1200px]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
=== 4. mainScript.m === &lt;br /&gt;
&lt;br /&gt;
This MATLAB script is an example on how to:&lt;br /&gt;
&lt;br /&gt;
* Read the accelerometer data of a subject for an activity &amp;amp; position&lt;br /&gt;
&lt;br /&gt;
* Read the Ground Truth Heel-Strike and Toe-Off gait events for the same&lt;br /&gt;
&lt;br /&gt;
* Plot all the signals&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
----&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 160%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;How to get the datasets?&amp;#039;&amp;#039;&amp;#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
&lt;br /&gt;
----------------------------------------------------------------------------------------------------------------------------------&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 110%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;1. Link to the Release Agreement and fill Registration Form:&amp;#039;&amp;#039;&amp;#039;  [http://islab.hh.se/mediawiki/Gait_database:_Release_agreement Click_Release Agreement]&amp;lt;/div&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 110%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;2. Link to download the datasets:&amp;#039;&amp;#039;&amp;#039;  Will be sent by Email after Registration Procedure is completed &amp;lt;/div&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 160%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;References&amp;#039;&amp;#039;&amp;#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
----------------------------------------------------&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 120%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;Please remember to include an appropriate citation to acknowledge the use of the database in all documents and papers that uses the MAREA Gait Database:&amp;#039;&amp;#039;&amp;#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
1. Siddhartha Khandelwal; Nicholas Wickström, &amp;quot;Evaluation of the performance of accelerometer-based gait event detection algorithms in different real-world scenarios using the MAREA gait database,&amp;quot; in Gait &amp;amp; Posture, DOI: 10.1016/j.gaitpost.2016.09.023, 2016&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 120%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;You may also want to read:&amp;#039;&amp;#039;&amp;#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
2. Siddhartha Khandelwal; Nicholas Wickström, &amp;quot;Gait Event Detection in Real-World Environment for Long-Term Applications: Incorporating Domain Knowledge into Time-Frequency Analysis,&amp;quot; in IEEE Transactions on Neural Systems and Rehabilitation Engineering , vol.PP, no.99, pp.1-1, 2016 &lt;br /&gt;
&lt;br /&gt;
IEEE url: [http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7423805&amp;amp;newsearch=true&amp;amp;queryText=siddhartha%20khandelwal]&lt;br /&gt;
DiVA url: [http://hh.diva-portal.org/smash/record.jsf?searchId=1&amp;amp;pid=diva2:909015]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
3. Siddhartha Khandelwal; Nicholas Wickström, &amp;quot;Identification of Gait Events using Expert Knowledge and Continuous Wavelet Transform Analysis&amp;quot;, in BIOSIGNALS 2014, 7th International Conference on Bio-inspired Systems and Signal Processing, Angers, France, March 3-6, 2014 &lt;br /&gt;
&lt;br /&gt;
DiVA url: [http://hh.diva-portal.org/smash/record.jsf?searchId=1&amp;amp;pid=diva2:688909]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;div style=&amp;quot;font-size: 160%;&amp;quot;&amp;gt;&amp;#039;&amp;#039;&amp;#039;Created By&amp;#039;&amp;#039;&amp;#039;&amp;lt;/div&amp;gt;&lt;br /&gt;
----------------------------------------------------&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
[http://islab.hh.se/mediawiki/Siddhartha_Khandelwal Siddhartha Khandelwal]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
----&lt;/div&gt;</summary>
		<author><name>Siddhartha</name></author>
	</entry>
	<entry>
		<id>https://mw.hh.se/caisr/index.php?title=Siddhartha_Khandelwal&amp;diff=2726</id>
		<title>Siddhartha Khandelwal</title>
		<link rel="alternate" type="text/html" href="https://mw.hh.se/caisr/index.php?title=Siddhartha_Khandelwal&amp;diff=2726"/>
		<updated>2016-09-30T12:28:09Z</updated>

		<summary type="html">&lt;p&gt;Siddhartha: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;{{Person&lt;br /&gt;
|Family Name=Khandelwal&lt;br /&gt;
|Given Name=Siddhartha&lt;br /&gt;
|Title=M.Sc&lt;br /&gt;
|Phone=+46-35-16-7667&lt;br /&gt;
|Position=PhD Candidate&lt;br /&gt;
|Email=siddhartha.khandelwal@hh.se&lt;br /&gt;
|Image=Sid.jpg&lt;br /&gt;
|Country=Sweden&lt;br /&gt;
|Office=E522&lt;br /&gt;
|url=https://www.linkedin.com/in/siddhartha-khandelwal-363b7718?trk=nav_responsive_tab_profile&lt;br /&gt;
|Subject=Human Motion Analysis using Wearable Sensors&lt;br /&gt;
}}&lt;br /&gt;
{{AssignProjects&lt;br /&gt;
|project=HMC2&lt;br /&gt;
}}&lt;br /&gt;
{{AssignSubjectAreas&lt;br /&gt;
|SubjectArea= Signal Analysis&lt;br /&gt;
}}&lt;br /&gt;
{{AssignApplicationAreas&lt;br /&gt;
|ApplicationArea= Health Technology&lt;br /&gt;
}}&lt;br /&gt;
{{AssignExaminer&lt;br /&gt;
|Examiner=Matlab with Applications (7.5 credits)&lt;br /&gt;
}}&lt;br /&gt;
{{AssignExaminer&lt;br /&gt;
|Examiner=Robotic Manipulators (Teaching Assistant, 7.5 credits)&lt;br /&gt;
}}&lt;br /&gt;
&lt;br /&gt;
[[Category:Staff]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;!--Remove or add comments --&amp;gt;&lt;br /&gt;
{{ShowPerson}}&lt;br /&gt;
{{InsertTeacher}}&lt;br /&gt;
{{InsertSubjAreas}}&lt;br /&gt;
{{InsertProjects}}&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== Open Access (code, data, etc.) ==&lt;br /&gt;
&lt;br /&gt;
Link to the page to download the &amp;#039;&amp;#039;&amp;#039; code for Gait Event Detection algorithm&amp;#039;&amp;#039;&amp;#039;: http://islab.hh.se/mediawiki/Gait_events &lt;br /&gt;
&lt;br /&gt;
Link to the page to download the &amp;#039;&amp;#039;&amp;#039;MAREA gait database&amp;#039;&amp;#039;&amp;#039;:  http://islab.hh.se/mediawiki/Gait_database &lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== About Siddhartha ==&lt;br /&gt;
&lt;br /&gt;
After finishing my Bachelors in Electronics from Vellore Institute of Technology, India[http://www.vit.ac.in/] and TU Dresden, Germany (Bachelor Thesis)[http://tu-dresden.de/en], I worked for almost an year in a Robotics Start-up called ThinkLabs at IIT, Bombay (India)[http://www.thinklabs.in/]. Then I received the Erasmus-Mundus scholarship &amp;quot;EMARO&amp;quot; [http://emaro.irccyn.ec-nantes.fr/] to pursue Masters in Robotics at WUT, Poland [http://www.pw.edu.pl/engpw] and ECN, France [http://www.ec-nantes.fr/version-anglaise/]. In 2012, I joined IS-Lab at Halmstad University to pursue my PhD on Human Motion Analysis using Wearable Sensors. My current research involves quantitative and qualitative analysis of human motion using wearable sensors (especially accelerometers) with focus on gait analysis, involving:&lt;br /&gt;
&lt;br /&gt;
* Gait event detection in real-world environments for long-term and continuous monitoring applications&lt;br /&gt;
* Activity classification and characterization of gait using wearable sensors &lt;br /&gt;
* Early prediction and continuous monitoring of neuro-physiological diseases such as Parkinson, cerebral palsy, etc&lt;br /&gt;
* Analyzing the performance and technique of elite athletes with special focus on bicycling&lt;br /&gt;
* Prediction of energy expenditure using wearable sensors&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
{{PublicationsList}}&lt;/div&gt;</summary>
		<author><name>Siddhartha</name></author>
	</entry>
	<entry>
		<id>https://mw.hh.se/caisr/index.php?title=Gait_database:_Release_agreement&amp;diff=2689</id>
		<title>Gait database: Release agreement</title>
		<link rel="alternate" type="text/html" href="https://mw.hh.se/caisr/index.php?title=Gait_database:_Release_agreement&amp;diff=2689"/>
		<updated>2016-09-27T08:50:31Z</updated>

		<summary type="html">&lt;p&gt;Siddhartha: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== Introduction ==&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
The MAREA Gait Database is meant to further research in gait analysis and related fields.  &lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== Release of the Database == &lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
This database can be downloaded as a zip file with password protection which shall be issued on a case-by-case basis. To receive the password, the requester must read and agree to the terms of usage of the database and fill the registration form. The form shall be evaluated and a link to download the database along with the password will be sent to the registered email address. Failure to observe this procedure may result in access being denied for the database.&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== Terms of Usage == &lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
The researcher(s) agrees to the following terms and conditions on the MAREA Gait Database:&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
1. &amp;#039;&amp;#039;&amp;#039;Redistribution&amp;#039;&amp;#039;&amp;#039;: The database, in whole or in part, will not be further distributed, published, copied, or disseminated in any way or form whatsoever, whether for profit or not. This&lt;br /&gt;
includes further distributing, copying or disseminating to a different facility or organizational unit in the requesting university, organization, or company.&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
2. &amp;#039;&amp;#039;&amp;#039;Modification and Commercial Use&amp;#039;&amp;#039;&amp;#039;: The database, in whole or in part, may not be modified or used for commercial purposes.&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
3. &amp;#039;&amp;#039;&amp;#039;Authorization&amp;#039;&amp;#039;&amp;#039;: All rights in and relating to the database remain with Halmstad University.&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
4. &amp;#039;&amp;#039;&amp;#039;Citation/Reference&amp;#039;&amp;#039;&amp;#039;: All documents and papers that report on research that uses the MAREA Gait Database will acknowledge&lt;br /&gt;
the use of the database by including an appropriate citation to the following:&lt;br /&gt;
	&lt;br /&gt;
&amp;#039;&amp;#039;Siddhartha Khandelwal; Nicholas Wickström, &amp;quot;Evaluation of the performance of accelerometer-based gait event detection algorithms in different real-world scenarios using the MAREA gait database,&amp;quot; (manuscript accepted) in Gait &amp;amp; Posture, DOI: 10.1016/j.gaitpost.2016.09.023&amp;#039;&amp;#039;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
6. &amp;#039;&amp;#039;&amp;#039;Publications&amp;#039;&amp;#039;&amp;#039;: A copy of all reports and papers that are for public or general release that use the database will be forwarded prior to or after release to Siddhartha Khandelwal (siddhartha.khandelwal@hh.se) and Nicholas Wickström (nicholas@hh.se).&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
7. &amp;#039;&amp;#039;&amp;#039;Indemnification&amp;#039;&amp;#039;&amp;#039;: Researcher agrees to indemnify, defend, and hold harmless Halmstad University, individually and collectively, from any and all losses, expenses, damages, demands and/or claims based upon any such injury or damage (real or alleged) and shall pay all damages, claims, judgments or expenses resulting from researcher’s use of the database.&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== Registration Form ==&lt;br /&gt;
&lt;br /&gt;
Link to fill the registration form: [https://goo.gl/forms/AFJhaP3qyUzBBnk63 click_registration form ]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
----&lt;/div&gt;</summary>
		<author><name>Siddhartha</name></author>
	</entry>
	<entry>
		<id>https://mw.hh.se/caisr/index.php?title=Gait_database:_Release_agreement&amp;diff=2688</id>
		<title>Gait database: Release agreement</title>
		<link rel="alternate" type="text/html" href="https://mw.hh.se/caisr/index.php?title=Gait_database:_Release_agreement&amp;diff=2688"/>
		<updated>2016-09-27T08:50:04Z</updated>

		<summary type="html">&lt;p&gt;Siddhartha: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== Introduction ==&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
The MAREA Gait Database is meant to further research in gait analysis and related fields.  &lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== Release of the Database == &lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
This database can be downloaded as a zip file with password protection which shall be issued on a case-by-case basis. To receive the password, the requester must read and agree to the terms of usage of the database and fill the registration form. The form shall be evaluated and a link to download the database along with the password will be sent to the registered email address. Failure to observe this procedure may result in access being denied for the database.&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== Terms of Usage == &lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
The researcher(s) agrees to the following terms and conditions on the MAREA Gait Database:&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
1. &amp;#039;&amp;#039;&amp;#039;Redistribution&amp;#039;&amp;#039;&amp;#039;: The database, in whole or in part, will not be further distributed, published, copied, or disseminated in any way or form whatsoever, whether for profit or not. This&lt;br /&gt;
includes further distributing, copying or disseminating to a different facility or organizational unit in the requesting university, organization, or company.&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
2. &amp;#039;&amp;#039;&amp;#039;Modification and Commercial Use&amp;#039;&amp;#039;&amp;#039;: The database, in whole or in part, may not be modified or used for commercial purposes.&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
3. &amp;#039;&amp;#039;&amp;#039;Authorization&amp;#039;&amp;#039;&amp;#039;: All rights in and relating to the database remain with Halmstad University.&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
4. &amp;#039;&amp;#039;&amp;#039;Citation/Reference&amp;#039;&amp;#039;&amp;#039;: All documents and papers that report on research that uses the MAREA Gait Database will acknowledge&lt;br /&gt;
the use of the database by including an appropriate citation to the following:&lt;br /&gt;
	&lt;br /&gt;
&amp;#039;&amp;#039;&amp;#039;Siddhartha Khandelwal; Nicholas Wickström, &amp;quot;Evaluation of the performance of accelerometer-based gait event detection algorithms in different real-world scenarios using the MAREA gait database,&amp;quot; (manuscript accepted) in Gait &amp;amp; Posture, DOI: 10.1016/j.gaitpost.2016.09.023&amp;#039;&amp;#039;&amp;#039;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
6. &amp;#039;&amp;#039;&amp;#039;Publications&amp;#039;&amp;#039;&amp;#039;: A copy of all reports and papers that are for public or general release that use the database will be forwarded prior to or after release to Siddhartha Khandelwal (siddhartha.khandelwal@hh.se) and Nicholas Wickström (nicholas@hh.se).&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
7. &amp;#039;&amp;#039;&amp;#039;Indemnification&amp;#039;&amp;#039;&amp;#039;: Researcher agrees to indemnify, defend, and hold harmless Halmstad University, individually and collectively, from any and all losses, expenses, damages, demands and/or claims based upon any such injury or damage (real or alleged) and shall pay all damages, claims, judgments or expenses resulting from researcher’s use of the database.&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== Registration Form ==&lt;br /&gt;
&lt;br /&gt;
Link to fill the registration form: [https://goo.gl/forms/AFJhaP3qyUzBBnk63 click_registration form ]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
----&lt;/div&gt;</summary>
		<author><name>Siddhartha</name></author>
	</entry>
</feed>